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Abstract

Effective scheduling of limited communication resources is one of the critical methods for data transmission in the Internet of things. However, the time slot utilization rate of many existing resource scheduling methods of Internet of things is not high. This article proposes a new efficient resource scheduling based on routing tree and detection matrix for Internet of things. In heterogeneous Internet of things, according to the different working modes and functions, the nodes are divided into Internet of things devices, routing nodes, and base station. We use time slot multiplexing to improve the time slot utilization of continuous transmission in Internet of things. First, the time slot allocation table in a round is obtained by the time slot scheduling based on the routing tree. Then, the collision matrix and the transmission matrix are established based on the time slot allocation table in a round. Finally, the minimum time slot scheduling in continuous rounds is determined based on the routing tree and the detection matrix. The experimental results show that the resource scheduling based on routing tree and detection matrix effectively improves the utilization of time slots and improves the throughput of the Internet of things.

Keywords

Internet of things resources scheduling routing matrix time slot

Introduction

With the development of 5G, more and more terminal devices and sensors are deployed in the Internet of things (IoT). It is estimated that the number of IoT connections will reach 4.1 billion in 2024, with an annual growth rate of 27%.¹ At present, the application of the IoT has penetrated many aspects of activities with the development of technology. IoT devices play a vital role in industry,² autonomous vehicles,³ agriculture,^4,5 healthcare,⁶ smart cities,⁷ and environment monitoring.⁸ The data transmission of the massive number of the sensor nodes is the main challenge in data collection of the IoT. Resource scheduling has great potential in ensuring IoT performance and avoiding transmission interference.⁹ Reducing the total time slot and improving the network throughput through reasonably use the limited resources in the communication network become one of the important research works in the IoT.

The resource scheduling methods generally need to compromise and optimize several designs because of differences of optimization objectives, interference models, and network environments. T Kim et al.¹⁰ proposed a low-complex greedy algorithm to expedite the scheduling process which considers the node’s lifetime for deciding the active set of nodes. Kumar et al.¹¹ reported resource scheduling during disaster situations. Priority-based stable matching algorithm is used for the allocation of resources for the corresponding activities.¹¹ Wang et al.¹² distributed a resource allocation solution for IoT which using an improved chaotic firefly algorithm that obtains the optimal location and working channel of secondary information gathering stations (SIGSs) to manage interference and resource allocation based on cognitive radio. Connectivity of nodes is critical for the IoT, as data collected need to be sent to the base station (BS). REN Moraes et al.¹³ proposed a link scheduling algorithm that minimizes the number of time slots needed to successfully schedule all the given links such that the nodes can communicate without interference in the signal to interference plus noise ratio (SINR) model. The various scheduling algorithm of IoT generally optimizes several designs because of differences of optimization objectives.

Resource scheduling usually aims to minimize wasting limited resources by efficiently allocating them among all nodes.¹⁴ In recent years, many research works on medium access control (MAC) protocol and resource allocation algorithms for the IoT have achieved some results.^15,16 Non-competitive scheduling is usually based on the time-division multiple access (TDMA) or the frequency-division multiple access (FDMA) or the code-division multiple access (CDMA) channel access mode to transfer data using a conflict-free manner. Yang and Wang¹⁷ investigated a dynamic allocation model for time and power resources in industrial IoT, reducing the energy loss of the communication system and ensuring the stability of communication between nodes. Liu et al.¹⁸ forward the optimal time scheduling of multiple modules which including spectrum sensing module, energy harvesting module, and ambient backscatter communication module (ABCom) by maximizing data transmission rate in IoT. Among them, the time slot allocation has become one of the main effective methods of resource scheduling.

Resource scheduling in a multi-hop environment poses a significant challenge due to the different resource requirement of each node. S Abdullah et al.¹⁹ proposed message scheduling with joint routing mechanism which is based on brokered architecture. The brokers choose scheduling strategy to transmit messages to the BS. P Gazori et al.²⁰ focused saving time and cost on the scheduling of fog-based IoT applications using deep reinforcement learning approach. However, the few resource scheduling algorithms give consideration to both the time slot multiplexing and the time slot utilization rate.

This article proposes a new efficient resource scheduling based on routing tree and detection matrix (RSRM) for Internet of things. We use time slot multiplexing to improve the time slot utilization of continuous transmission in the IoT. The main contributions of this article are as follows. (1) We presented time slot scheduling based on the routing tree (TSRT). The TSRT obtains the time slot allocation table in a round. (2) New RSRM in IoT is presented which use time slot multiplexing to improve the time slot utilization of continuous transmission in IoT. (3) We conducted detailed simulation experiments to investigate the performance of the proposed TSRT and RSRM scheduling. The RSRM effectively improves the utilization of time slots and improves network throughput of IoT.

The rest of this article is organized as follows. Section “Related work” discusses the related work. Section “System model” gives the system model. In section “Proposed algorithm,” the RSRM in IoT is presented. We provide theoretical analysis in section “Theoretical analysis.” Section “Experiments” presents the experiments. Finally, the conclusions are given in section “Conclusion.”

Related work

In recent years, many researchers have addressed resource allocation in IoT. Samanta and Tang²¹ presented a dynamic micro-service scheduling (DYME) for the mobile edge computing in IoT platform and discussed the computational complexity of the scheduling algorithm in IoT. Olatinwo and Joubert²² presented novel approaches to the allocation of resources in Internet of things sensor network (IoTSN) systems applied to water-quality monitoring for optimization and more sustainable utilization of resources. IoT devices usually need to communicate with each other and some remote gateways through multi-hop communications. Qin et al.²³ proposed a dual-interface dual-pipeline scheduling (DIPS) scheme that leverages low-power ZigBee interfaces to wake up a data pipeline constructed by high-power Wi-Fi interfaces fast pipelined data delivery in the IoT.

Wireless sensor network (WSN) is an essential component of IoT applications. To improve the utilization of network resources and reduce the delay of data transmission, some resource allocation schemes perform scheduling on channels and time slots. Gabale et al.²⁴ indicated PIP (packets in pipe) algorithm which is suitable for multi-hop and multi-channel networks, especially for directional linear networks. Xu et al.²⁵ proposed the use of slot reuse technology to improve the efficiency of resource scheduling in industrial WSNs. In this polling slot allocation, the network consists of subnetworks (FNs), backbone (BN).

Some researchers focus on collision-free resource allocations which are organized in a tree topology. Lee and Cho²⁶ proposed tree-based time division multiple access MAC algorithm (tree TDMA) using time and frequency allocations. The tree TDMA supports full-duplex communication of voice and data. Osamy et al.²⁷ proposed Effective TDMA scheduling for tree-based data collection using a genetic algorithm (ETDMA-GA). The genetic algorithm (GA) has been utilized to solve the generation of TDMA scheduling in ETDMA-GA.

Resource scheduling in a fair and efficient is major problem in multi-hop multi-channel networks. TDMA scheduling is extensively used for data delivery with the aim of minimizing the time slots for transporting data in multi-hop network.²⁸ Xu et al.²⁹ distributed a real-time scheduling in duty-cycled multi-hop sensor networks. The authors focused on periodic queries with sufficiently long time horizon in duty-cycled sensor networks in the presented scheduling. Multi-channel communication is an essential means to improve the efficiency of IoT. Tan et al.³⁰ studied a multi-channel transmission scheme of a green IoT in underground mining and formulated it as a multi-channel multi-radio time slot co-scheduling problem. Zhang et al.³¹ proposed coloring route-tree based resource allocation algorithm for industrial WSNs. Gao et al.³² proposed an edge-based channel allocation (ECA) for unreliable IoT networks.

At present, a trend is the running different applications on heterogeneous sensor nodes deployed in network in order to better exploit the physical network infrastructure. Li et al.³³ present the resource allocation in heterogeneous WSNs (SACHSEN) algorithm to make effective task-sensor assignments explicitly considering the performance requirements of application.

The implementation of data fusion plays a crucially important role in reducing the level of network traffic. F Alam et al.³⁴ reviewed literatures on data fusion for IoT with a particular focus on mathematical methods and specific IoT environments. Jiang et al.³⁵ proposed fairness-based packing of industrial IoT data in permissioned blockchains. The transaction packing algorithm not only achieves better fairness but also reduces the average response time as well.

Although the existing resource scheduling can satisfy the data transmission without conflict, but the utilization of time slot is not very high in the continuous transmission. In addition, many existing scheduling methods assume that nodes in network are homogeneous. This article proposes a new resource scheduling scheme in IoT (RSRM) where the nodes are divided into sensor nodes, routing nodes, and BS. The RSRM use time slot multiplexing to improve the time slot utilization of continuous transmission in IoT.

System model

This section mainly introduces the network model, the transmission interference model, and the symbol representation of RSRM in the IoT.

Network model

IoT is composed of a large number of nodes. According to the different working modes and functions, these nodes are divided into three types—IoT devices, routing nodes, and BS, as shown in Figure 1. The IoT devices collect and transmit data. The routing nodes are responsible for data fusion and routing. The BS collects data of all the nodes in each round.

Figure 1.

Topology of network.

In this article, the system model is based on the following assumptions:

The position of nodes is fixed.

The BS collects the data of all nodes.

The routing nodes fuse, pack, and forward the received data.

The length of the time slot is determined by the longest packet, which is adjustable.

Given an IoT network G(V, E), where the V represents a set of nodes in the network and the E represents a set of links, $E \subseteq V \times V$ . The set of available channels is C = {C₁, C₂,…, C_k}. The optimization problem of resource scheduling can be described as the transmission of data to the BS in the minimum time slot and no conflict, which is shown as follows

$\begin{array}{l} \min (\sum_{R = 1}^{N} T s_{R}) \\ s . t . \\ C 1 : f (u, v, T, C) \in {0, 1}, \forall u \in V, \forall v \in V \\ C 2 : {Σ v}_{\in N B r_{u}} f (u, v, T, C) \leq 1, \forall u \in V, \forall v \in V \\ C 3 : {Σ v}_{\in N B r_{u}} {f (u, v, T, C) + f (v, u, T, C)} \leq 1, \\ \forall u \in V, \forall v \in V \end{array}$ (1)

where the Ts_R represent the time slots in a round, the R is the number of rounds, and the NBr_u represents the all neighbors of node u. In the constraint C1, f (u, v, T, C) = 1 indicates that at time T, node u uses channel C to send data to node v. f (u, v, T, C) = 0 means that no operation is taken. The C2 indicates that in a time slot, a node u can only communicate with at most one neighbor node. The C3 indicates that a node only be either sending state or receiving state in a time slot.

Transmission interference model

Resource scheduling aims to transmit data while avoiding transmission conflicts through efficient resource allocation. This investigation is based on the graph-based protocol model, which includes a primary conflict and a secondary conflict. The primary conflict affects the range among one-hop neighbors, which occurs when one node attempts to do multiple operations at the same time as shown in Figure 2(a). The secondary conflict affects the range among the two-hop neighbors, which use the same channel to transmit. The secondary conflict is usually considered to be a hidden terminal problem, which is shown in Figure 2(b).

Figure 2.

Transmission interference model: (a) primary conflict and (b) secondary conflict.

Symbolic representation

Some symbolic representation which is used in this article is shown in Table 1.

Table 1.

Symbol representation.

Symbol	Descriptions
Num	The total number of nodes.
N_i	Node i in IoT.
C(N_i)	Channel of N_i.
T_i	Time slot i in a round. T is the time slot table in a round. T = {T₁, T₂,…, T_max}. The T_max is the total number of time slots used in a round.
R	The number of continuous rounds.
SR(N_i,T_i)	SR(N_i,T_i) is time slot allocation table in a round. The N_i transmits data in slot T_i in a round.
CM	Conflict matrix CM derived by network topology and SR(N_i,T_i), CM_ij ∈ {0, 1}.
TM	Transfer matrix TM derived by SR(N_i,T_i) and CM, TM_ij ∈ {0, 1}.
DM	Detection matrix DM is the matrix for detecting periodic overlap of slots in continuous rounds which is derived by CM and TM.
U	The upper limit of non-overlapping time slots is obtained by matrix operation. The time slots can be reused after the U slot.
TS_i	TS_i is the time slot i in continuous rounds. TS is the time slot table in continuous round. TS = {TS₁, TS₂,…, TS_max}. The TS_max is the total number of slots used by the network in continuous rounds.
S(N_i,TS_i)	S(N_i,TS_i) is the final time slot allocation table. The N_i transmits data in slot TS_i in continuous rounds.

Proposed algorithm

In this section, the RSRM for IoT is presented. The structure of resource scheduling is shown in Figure 3. The nodes are divided into IoT devices, routing nodes, and BS. First, the time slot allocation table in a round is obtained by the scheduling based on the routing tree (TSRT). Then, the collision matrix and the transmission matrix are established based on the time slot allocation table. Finally, the minimum time slot scheduling in continuous rounds is determined based on the routing tree and the detection matrix.

Figure 3.

Structure of resource scheduling based on routing tree and detection matrix.

The main idea of the RSRM is as follows:

The time slot allocation table in a round (SR(N_i,T_i)) is obtained by the TSRT.

The conflict matrix (CM) is established based routing tree and topology of IoT.

The transfer matrix (TM) is established based on SR(N_i,T_i).

The detection matrix (DM) is obtained according to the CM and TM. The upper limit of the non-overlapping period (U) is calculated to ensure the normal execution of the time interval.

The minimum time slot allocation table in continuous rounds (S(N_i,TR_i)) is determined.

TSRT

The time slot allocation table in a round is obtained by the TSRT. The RSRM in the IoT is based on the TSRT. The routing tree is constructed according to the topology of the network. An example of a routing tree is shown in Figure 4. The nodes in IoT are divided into the IoT devices, routing nodes, and BS.

Figure 4.

Example of routing tree of IoT.

The channel allocation based on graph coloring is proposed in our previous work.³¹ Nodes within the range of one hop are assigned in different channels. The set of available channels is C = {C₁, C₂,…, C_k}. The K is the total number of available channels. In the coloring phase, all channels are assumed to be available. The color corresponds to channel which is shown in equation (2)

$\begin{matrix} C (N c_{i}) = \\ {\begin{matrix} C_{1}, & if N c_{i} = BS, i = 1 \\ C_{i + μ}, & if 1 < i < K \\ C_{i + 1 - mK + μ} & if mK < i < (m + 1) K, m = 1, 2, . . ., i > K \end{matrix} \end{matrix}$ (2)

where the Nc_i is sorted from top to bottom corresponds to the node N_i of the IoT. The initial value of µ is 0. If the obtained channel is the same as that of the relay node, the µ is increased by 1. This process does not stop until BS, and all the routing nodes are assigned to the channels. Figure 5 shows the channel allocation based on graph coloring. The different colors represent the different channels. The BS chooses the channel C₁. The channels of routing nodes are calculated according to equation (2). The channel of the IoT device is consistent with their parent routing node, and the conflict is avoided by TDMA.

Figure 5.

Channel allocation based on graph coloring.

We presented the TSRT where each node is assigned a time slot in a round. The time slot allocation table in a round (SR(N_i,T_i)) is obtained by the TSRT which is shown in Algorithm 1.

Algorithm 1. Time slot scheduling based on routing tree (TSRT)
1. INPUT: Number of nodes (Num) and routing tree of IoT2. OUTPUT: Time slot allocation table in a round SR(N_i,T_i)3. SR(N_i,T_i) ← Φ4. T_i ← 15. All the node are marked in non-traversed.6. Num’ ← Num7. while (Num’ ≠ 1) do8. for (non-traversed leaf node) do9. Find the leaf node N_i with the smallest ordinal number.10. Time slot T_i is allocated to N_i.11. The information is added to the SR(N_i,T_i).12. Corresponding node is marked in traversed.13. Leaf node N_i is deleted from the topology.14. Num’ ← Num’ − 115. end for16. T_i ← T_i + 117. All the nodes are marked in non-traversed.18. end while19. The time slot allocation table of a round SR(N_i,T_i) is obtained.20. returnSR(N_i,T_i)

Algorithm 1. Time slot scheduling based on routing tree (TSRT)

1. INPUT: Number of nodes (Num) and routing tree of IoT2. OUTPUT: Time slot allocation table in a round SR(N_i,T_i)3. SR(N_i,T_i) ← Φ4. T_i ← 15. All the node are marked in non-traversed.6. Num’ ← Num7. while (Num’ ≠ 1) do8. for (non-traversed leaf node) do9. Find the leaf node N_i with the smallest ordinal number.10. Time slot T_i is allocated to N_i.11. The information is added to the SR(N_i,T_i).12. Corresponding node is marked in traversed.13. Leaf node N_i is deleted from the topology.14. Num’ ← Num’ − 115. end for16. T_i ← T_i + 117. All the nodes are marked in non-traversed.18. end while19. The time slot allocation table of a round SR(N_i,T_i) is obtained.20. returnSR(N_i,T_i)

The specific steps of TSRT are as follows:

S₁: the routing tree is obtained according to the topology of network.

S₂: the initial value of the time slot allocation table in a round is Φ. SR(N_i,T_i) ← {Φ}, T_i ← 1. All nodes are marked in non-traversed.

S₃: we set a temporary number of nodes Num’ ← Num. The Num is the number of nodes in the network.

S₄: if Num’ = 1, we stop the current time slot scheduling process and skip to S₈. Otherwise, S₅ is executed.

S₅: starting from the BS, depth traversal algorithm is use to find the leaf node N_i with the smallest ordinal number on the non-traversed branches, and the time slot T_i is allocated to N_i. The information is added to SR(N_i,T_i) and the corresponding branch is marked in traversed.

S₆: the allocated leaf node N_i is deleted from the temporary topology, and other nodes become leaf nodes again. We set Num’ ← Num’ − 1. If all the branches of the routing tree are traversed, skip to S₇. Otherwise, skip to S₅.

S₇: the T_i is added to the time slot table T and T_i ← T_i + 1. T = {T₁, T₂,…,T_i}. All the nodes are marked in non-traversed, skip to S₄.

S₈: we obtain and store the time slot allocation table of a round SR(N_i,T_i).

The process of time slot allocation in TSRT is shown in Figure 6. The adjacent nodes communicate with multi-channel, and the transmission of nodes does not interfere with each other. In Figure 6, The N₁, N₂, N₄, N₆, and N₇ send data to their relay nodes in the first time slot (T₁). The N₃, N₅, N₈, N₁₀, and N₁₃ send data to their relay nodes in the second time slot (T₂). Until time slot T₇, all data are transmitted to the BS in a round. The time slot allocation table in a round SR(N_i,T_i) is obtained as shown in Table 2. We get that the total number of time slots used in a round is T_max = 7 according to TSRT.

Figure 6.

Process of time slot allocation in a round.

Table 2.

Time slot allocation table in a round SR(N_i,T_i).

Node	N₁	N₂	N₃	N₄	N₅	N₆	N₇	N₈	N₉	N₁₀	N₁₁	N₁₂	N₁₃	N₁₄	N₁₅	N₁₆	N₁₇	N₁₈	N₁₉

RSRM

The scheduling strategy of the above TSRT algorithm can ensure that all data are transmitted to the BS with the least number of slots in a single round. However, the performance of TSRT in continuous rounds is poor because each node needs to wait for a complete round before transmitting data to the other node.

We propose a new efficient RSRM to improve TSRT. We use the overlap of time slots to reduce the interval between two adjacent rounds, and improve the overall transmission efficiency of the network. The improved scheme aims to find the minimum time slot interval and ensure that no transmission conflict occurs between nodes.

To determine whether a conflict exists between adjacent rounds, the CM is introduced. The CM is the Num − 1 order matrix, where the Num is the total number of nodes. The CM_ij represents that whether node i and node j interfere with each other in data transmission. The CM is defined as follows

$C M_{ij} = {\begin{matrix} 1, & If N_{i} and N_{j} interfere with each other \\ 0, & Otherwise \end{matrix}$ (3)

The TM is introduced which records time slot allocation of nodes in a round. The TM is the (Num − 1) × T_max order matrix, where the T_max represents the total number of slots in a round in above SR(N_i,T_i). The TM_ij denotes that N_i transmits data to its parent node in T_j. The definition of TM is as follows

$T M_{ij} = {\begin{matrix} 1, & If N_{i} transmits data in slot T_{j} \\ 0, & Otherwise \end{matrix}$ (4)

We introduce the DM to detect whether there is communication interference in time slot multiplexing during time slot allocation in continuous rounds. The DM is the (Num − 1) × T_max order matrix. As shown in equation (5), it indicates that there is communication interference among N_i and other nodes in the T_j when the value of DM_ij is not equal to 0 or 1. We define the time slot interval matrix TM’ which moving TM some columns to the right. We set the initial value of TM’ TM. The new TM’ deletes the rightmost column of the old TM’ and splices the zero vectors with the same number of rows in the leftmost column. The DM is defined as equation (6)

$D M_{ij} = {\begin{matrix} ! (0 | | 1), & If N_{i} interfere with other nodes in T_{j} \\ 0 | | 1, & Otherwise \end{matrix}$ (5)

$D M_{ij} = \sum_{k = i}^{j} C M_{ik} \times T {M'}_{kj}$ (6)

The RSRM is based on the TSRT. We use the overlap of time slots to reduce the interval between two adjacent rounds. The final time slot allocation table S(N_i,TR_i) is obtained by the RSRM which is shown in Algorithm 2.

Algorithm 2. Resource scheduling based on routing tree and detection matrix (RSRM)
1. INPUT: Time slot allocation table in a round SR(N_i,T_i)2. OUTPUT: The final time slot allocation table in continuous rounds S(N_i,TR_i)3. //Create the Conflict Matrix CM4. fori ← 0 to Num do //The Num is the total number of nodes5. forj ← 0 to Num do6. ifN_i and N_j interfere with each other then7. CM[i][j] ← 18. else9. CM[i][j] ← 010. end if11. end for12. end for13. //Create Transfer Matrix TM14. fori ← 0 to Num do15. forj ← 0 to T_maxdo //The T_max is the max slot in a round16. ifN_i transmits date in T_j then17. TM[i][j] ← 118. else19. TM[i][j] ← 020. end if21. end for22. end for23. //Create the Detection Matrix DM24. TM’ ← TM // Initializing interval matrix TM’25. U ← 0 //Setting the initial value of U26. DM_ij ← {2} //Setting the initial value of DM_ij27. while max (DM_ij) > 1 do28. fori ← 0 to Num do29. TM’[i][0] ← 030. forj ←0 to T_maxdo31. TM’[i][j] ← TM’[i][j − 1]32. end for33. end for34. $D M_{ij} = \sum_{k = 1}^{j} C M_{ik} \times T M'_{kj}$ 35. U ← U + 136. end while37. The final time slot allocation table in continuous rounds S(N_i,TR_i) is obtained.38. returnS(N_i,TR_i)

Algorithm 2. Resource scheduling based on routing tree and detection matrix (RSRM)

1. INPUT: Time slot allocation table in a round SR(N_i,T_i)2. OUTPUT: The final time slot allocation table in continuous rounds S(N_i,TR_i)3. //Create the Conflict Matrix CM4. fori ← 0 to Num do //The Num is the total number of nodes5. forj ← 0 to Num do6. ifN_i and N_j interfere with each other then7. CM[i][j] ← 18. else9. CM[i][j] ← 010. end if11. end for12. end for13. //Create Transfer Matrix TM14. fori ← 0 to Num do15. forj ← 0 to T_maxdo //The T_max is the max slot in a round16. ifN_i transmits date in T_j then17. TM[i][j] ← 118. else19. TM[i][j] ← 020. end if21. end for22. end for23. //Create the Detection Matrix DM24. TM’ ← TM // Initializing interval matrix TM’25. U ← 0 //Setting the initial value of U26. DM_ij ← {2} //Setting the initial value of DM_ij27. while max (DM_ij) > 1 do28. fori ← 0 to Num do29. TM’[i][0] ← 030. forj ←0 to T_maxdo31. TM’[i][j] ← TM’[i][j − 1]32. end for33. end for34.

D M_{ij} = \sum_{k = 1}^{j} C M_{ik} \times T M'_{kj}

35. U ← U + 136. end while37. The final time slot allocation table in continuous rounds S(N_i,TR_i) is obtained.38. returnS(N_i,TR_i)

The specific steps of RSRM are as follows:

S₁: the TSRT algorithm has been executed, and the time slot allocation table in a round SR(N_i,T_i) is obtained by Algorithm 1.

S₂: the CM is obtained by equation (3).

S₃: the TM is obtained by equation (4).

S₄: we define the time slot interval matrix TM’ and set the initial value TM’ ← TM. The upper limit of the non-overlapping round U is recorded. We set the initial value U ← 0, DM ← {2}.

S₅: we delete the rightmost column of the matrix TM’ and splice the zero vectors with the same number of rows in the leftmost column.

S₆: the detection matrix DM is computed as equation (6).

S₇: if the element of matrix DM_ij is greater than 1, skip to S₃. Otherwise, the RSRM algorithm is stopped. The U is the required slot interval in continuous rounds. The T_max − U time slots are reused between adjacent rounds.

S₈: the final time slot allocation table in continuous rounds S(N_i,TR_i) is obtained according to the upper limit of non-overlapping time slots U.

Examples of RSRM

We take the routing tree and channel allocation above in section “TSRT” (Figure 5) as an example. The CM is obtained as shown in Figure 7. According to TSRT and Table 2, the TM is obtained by equation (4). The initial value of TM’ is TM. The new TM’ deletes the rightmost column of the TM’ and splices the zero vectors in the leftmost column.

Figure 7.

Conflict matrix (CM).

The detection matrix DM is calculated according to equation (6). As shown in Table 3, some elements of DM are greater than 1 when U = {1, 2, 3}. But, all values of DM_ij are equal to 0 or 1 when U = 4, which indicates that there is no communication interference between nodes. Hence, U = 4 is the required time slot interval in continuous rounds. Time slots in continuous rounds can be reused after the U time slots.

Table 3.

Detection matrix (DM).

U = 1	U = 2	U = 3	U = 4
1 2 1 0 0 0 0	1 1 1 1 0 0 0	1 1 0 1 1 0 0	1 1 0 0 1 1 0
1 2 2 1 0 0 0	1 1 2 1 1 0 0	1 1 1 1 1 1 0	1 1 1 0 1 1 1
0 1 2 1 0 0 0	0 1 1 1 1 0 0	0 1 1 0 1 1 0	0 1 1 0 0 1 1
1 2 1 1 1 0 0	1 1 1 2 0 1 0	1 1 0 2 1 0 1	1 1 0 1 1 1 0
0 1 1 1 1 0 0	0 1 0 2 0 1 0	0 1 0 1 1 0 1	0 1 0 1 0 1 0
1 2 1 0 0 0 0	1 1 1 1 0 0 0	1 1 0 1 1 0 0	1 1 0 0 1 1 0
1 2 2 2 1 0 0	1 1 2 2 1 1 0	1 1 1 2 1 1 1	1 1 1 1 1 1 1
0 1 2 2 1 0 0	0 1 1 2 1 1 0	0 1 1 1 1 1 1	0 1 1 1 0 1 1
0 0 1 2 1 0 0	0 0 1 1 1 1 0	0 0 1 1 0 1 1	0 0 1 1 0 0 1
0 1 2 1 0 0 0	0 1 1 1 1 0 0	0 1 1 0 1 1 0	0 1 1 0 0 1 1
0 0 1 2 2 1 0	0 0 1 1 2 1 1	0 0 1 1 1 1 1	0 0 1 1 1 0 1
0 0 0 1 2 1 0	0 0 0 1 1 1 1	0 0 0 1 1 0 1	0 0 0 1 1 0 0
0 1 1 1 2 1 0	0 1 0 2 1 1 1	0 1 0 1 2 0 1	0 1 0 1 1 1 0
0 0 0 1 2 1 0	0 0 0 1 1 1 1	0 0 0 1 1 0 1	0 0 0 1 1 0 0
0 0 1 1 1 2 1	0 0 1 0 2 1 1	0 0 1 0 1 2 0	0 0 1 0 1 1 1
0 0 0 0 1 2 1	0 0 0 0 1 1 1	0 0 0 0 1 1 0	0 0 0 0 1 1 0
0 0 0 0 1 1 1	0 0 0 0 1 0 2	0 0 0 0 1 0 1	0 0 0 0 1 0 1
0 0 0 0 0 1 2	0 0 0 0 0 1 1	0 0 0 0 0 1 1	0 0 0 0 0 1 1
0 0 0 0 0 0 1	0 0 0 0 0 0 1	0 0 0 0 0 0 1	0 0 0 0 0 0 1

According to Table 2, in TSRT for six continuous rounds (R = 6), the total number of slots is 42 (T_max = 7, TR_max = 7 × 6 = 42). In RSRM, we use the overlap of time slots to reduce the interval between two adjacent rounds and find the upper limit of non-overlapping time slots U. In the case, U = 4, which is shown in Table 3, we multiplex T_max − U slots in each round. As shown in Table 4, we overlap 3 (T_max − U = 7 − 4 = 3) time slots in each round. The total number of time slots is 27 when R = 6 (TR_max = 27). Table 5 shows the final time slot allocation table. The RSRM saves 15 (42–27 = 15) time slots than TSRT for six continuous rounds. We assume that the unit time slot Δt = 1.

Table 4.

Time slot allocation table in continuous round.

R	Slot
	TR₁	TR₂	TR₃	TR₄	TR₅	TR₆	TR₇	TR₈	TR₉	TR₁₀	TR₁₁	TR₁₂	TR₁₃	TR₁₄	TR₁₅	TR₁₆	TR₁₇	TR₁₈	TR₁₉	TR₂₀	TR₂₁	TR₂₂	TR₂₃	TR₂₄	TR₂₅	TR₂₆	TR₂₇

Table 5.

Final time slot allocation table S(N_i,TR_i).

R	Node
	N₁	N₂	N₃	N₄	N₅	N₆	N₇	N₈	N₉	N₁₀	N₁₁	N₁₂	N₁₃	N₁₄	N₁₅	N₁₆	N₁₇	N₁₈	N₁₉

The less U, the more time slots saved in continuous rounds in RSRM. The U is related to the topology and the routing tree of the network. The IoT devices switch to sleep state and reducing energy consumption and extending the lifetime of the whole network when they do not need to transmit or receive data.

Theoretical analysis

Theorem 1

The total time slot of the whole cycle of the network is TR_max. The total time slot of the network is TRI_max, when R tends to infinity

$T R_{max} = (R \times U + T_{max} - U) \times Δ t$ (7)

$TR I_{max} \approx R \times U \times Δ t$ (8)

where the T_max is the time slot in a round which is obtained by the TSRT. The U is upper limit of the non-overlapping period which is obtained by the RSRM. The Δt is length of unit time slot. The total cycle of the network has R rounds.

Proof

In RSRM, we use the overlap of time slots to reduce the interval between two adjacent rounds, and improve the overall transmission efficiency of the network. The upper limit of the non-overlapping period (U) is the minimum time slot interval which is calculated by RSRM. Due to the use of optimization, T_max − U time slots are reused between adjacent rounds. The total time slot of the whole life cycle is as follows

$\begin{matrix} T R_{max} = [R \times T_{max} - (R - 1) \times (T_{max} - U)] \times Δ t \\ = (R \times U + T_{max} - U) \times Δ t \end{matrix}$ (9)

The average slot time in per round (T_arr) is

$T_{arr} = \frac{(R \times U + T_{max} - U) \times Δ t}{R}$ (10)

When the R tends to infinity, the T_arr is

$\begin{matrix} lim_{R \to \infty} T_{arr} = lim_{R \to \infty} (\frac{(R \times U + T_{max} - U) \times Δ t}{R}) \\ = U \times Δ t \end{matrix}$ (11)

Accordingly, the total time slot of the whole life cycle is TRI_max ≈ R × U × Δt when R tends to infinity.

Theorem 2

The throughput of the whole network is TP when R tends to infinity

$TP \approx \frac{(Num - 1) \times d}{U \times Δ t}$ (12)

where the d is the length of data transmitted by each node in a round. The Num is the total number of IoT devices.

Proof

Throughput is defined as the amount of data transmitted in a unit time. In a single round, every IoT devices need to transmit a packet to the BS. Therefore, the throughput of the RSRM in one round (TP_u) is as follows

$T P_{u} = \frac{(Num - 1) \times d}{T_{max} \times Δ t}$ (13)

For continuous multiple rounds, RSRM makes the rounds overlap and speeds up data transmission. The throughput of the whole network in continuous R rounds (TP_R) is as follows

$T P_{R} = \frac{R \times (Num - 1) \times d}{[R \times T_{max} - (R - 1) \times (T_{max} - U)] \times Δ t}$ (14)

Take the limit of R to get

$\begin{matrix} lim_{R \to \infty} T P_{R} = lim_{R \to \infty} (\frac{R \times (Num - 1) \times d}{[R \times T_{max} - (R - 1) \times (T_{max} - U)] \times Δ t}) \\ = lim_{R \to \infty} (\frac{R \times (Num - 1) \times d}{(R \times U + T_{max} - U) \times Δ t}) \\ = \frac{(Num - 1) \times d}{U \times Δ t} \end{matrix}$ (15)

So, the throughput of the whole network is $TP \approx (Num - 1) \times d / U \times Δ t$ when the round tends to infinity. By Theorems 1 and 2, we can see that the smaller upper limit of non-overlapping time slots (U), the less total time slot has been used, and the higher throughput will be.

Experiments

The nodes are distributed in the area 100 m × 100 m. The nodes of the network include IoT devices, routing nodes, and BS. The IoT devices are responsible for data collection and transmission. The routing nodes fuse and forward the received data. The BS collects the data of all nodes. The simulation parameters are given in Table 6.

Table 6.

Simulation parameters.

Parameter	Value
Deployment field	100 m × 100 m
N (number of nodes)	10~50
Deployment method of nodes	Random
Initial energy	5 J
d (the length of data in a round)	125 byte
E_elce	50 nJ/bit
ε_fs	10 pJ/bit/m²
ε_mp	0.0013 pJ/bit/m⁴
E_DA	5 nJ/bit/signal
Δt (the length of unit time slot)	0.1 s

The example of channel and time slot allocation in section “Experiments” in continuous three rounds which is according to RSRM is shown in Figure 8. The IoT devices are at the lowest level, and the BS is on the top. The different color represents the different channels and the Ti represents the time slots. The IoT devices use the same channel as its relay node. The routing node jumps to the channel of the corresponding relay node before transmitting data.

Figure 8.

Channel and time slot allocation.

Figure 9 shows the total time slots in continuous rounds in RSRM when the U increases from 1 to 5 at different T_max and rounds (R). The total time slot of the whole cycle of the network (TR_max) is obtained by the RSRM according to equation (7). The total number of time slots in a round (T_max) is obtained by the TSRT. The U is the upper limit of the non-overlapping period which is related to the distribution of nodes, the routing tree of network, and the number of nodes. The total time slot of IoT increases with the value of the T_max, U, and R.

Figure 9.

The total time slot of RSRM in different U.

Figure 10 shows the average total time slots in continuous rounds used in RSRM when the number of nodes increases from 10 to 50, and the rounds increase from 1 to 8. Theoretically, the total number of time slots is related to the routing tree of network instead of the number of nodes. We take the average total time slots where T_max and U are relatively large when there are many nodes. The average total time slot of IoT increases with the number of nodes and rounds.

Figure 10.

Average total time slot of RSRM in different rounds.

The RSRM uses the overlapping of time slots to reduce the interval between two adjacent rounds and improve the network’s overall transmission efficiency. The minimum slot allocation in continuous rounds is determined based on the matrix calculation to ensure the normal execution of the time internal. As shown in Figure 11, the total time slot of RSRM is significantly reduced compared with the TSRT.

Figure 11.

The total time slot of RSRM and TSRT.

Figure 12 shows the network throughput of the RSRM and TSRT. It indicates that the network throughput of RSRM is significantly improved compared with the TSRT. With the increase in network size, the network throughput increases.

Figure 12.

Network throughput of RSRM and TSRT.

Conclusion

A new RSRM in IoT is proposed in this article. The RSRM is based on the time slot allocation in a round is obtained by the TSRT. The DM is obtained according to the collision matrix and the transmission matrix. The minimum time slot scheduling in continuous rounds is determined by RSRM which uses the overlapping of time slots to reduce the interval between two adjacent rounds in continuous rounds and improve the time slot utilization of IoT. Simulation results show that the RSRM scheduling algorithm effectively improves the utilization of time slots and improves network throughput of IoT. In further research, clustering-based scheduling algorithm is introduced when the number of nodes is large in IoT.

Footnotes

Handling Editor: Yanjiao Chen

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research,authorship,and/or publication of this article: This work was supported,in part,by the National Natural Science Foundation of China (NSFC) project (no. 61671056) and the Scientific Research Projects of Ordos Institute of Technology (no. KYYB2018006).

ORCID iDs

Hongying Bai

Xiaotong Zhang

References

Vikhrova

Pizzi

Sinitsyn

, et al. An analytic approach for resource allocation of IoT multicast traffic. In: Proceedings of the ACM MobiHoc workshop on pervasive systems in the IoT era, Catania, 2 July 2019, pp.25–30. New York: ACM.

Musengimana

Yoo

. Real-time scheduling based on multi-channel communication in IEEE 802.15.4 industrial Internet of things (IIoT). In: Proceedings of the 2018 international conference on fuzzy theory and its applications (iFUZZY), Daegu, South Korea, 14–17 November 2018, pp.371–374. New York: IEEE.

Xiao

Krunz

Shu

Multi-operator network sharing for massive IoT. IEEE Commun Mag 2020; 57(4): 96–101.

Gupta

Khosravy

Patel

, et al. Economic data analytic AI technique on IoT edge devices for health monitoring of agriculture machines. Appl Intell 2020; 50: 3990–4016.

De Souza

PSS

Rubin

Hohemberger

, et al. Detecting abnormal sensors via machine learning: an IoT farming WSN-based architecture case study. Measurement 2020; 164: 108042.

Gope

Hwang

BSN-Care: a secure IoT-based modern healthcare system using body sensor network. IEEE Sens J 2016; 16: 1368–1376.

El-Hosseini

ZainEldin

Arafat

, et al. A fire detection model based on power-aware scheduling for IoT-sensors in smart cities with partial coverage. J Amb Intel Hum Comp 2021; 12: 2629–2648.

Kim

Lee

Kim

, et al. Adaptive packet scheduling in IoT environment based on Q-learning. J Amb Intel Hum Comp 2020; 11: 2225–2235.

Kavitha

Suseendran

. Priority based adaptive scheduling algorithm for IoT sensor systems. In: Proceedings of the 2019 international conference on automation, computational and technology management (ICACTM), London, 24–26 April 2019, pp.361–366. New York: IEEE.

10.

Kim

Qiao

Choi

. Energy-efficient scheduling of Internet of things devices for environment monitoring applications. In: Proceedings of the 2018 IEEE international conference on communications (ICC), Kansas City, MO, 20–24 May 2018, pp.1–7. New York: IEEE.

11.

Kumar

Zaveri

Choksi

Activity based resource allocation in IoT for disaster management. In: Patel

Gupta

(eds) Future Internet technologies and trends, vol. 220 (Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering). Heidelberg: Springer, 2017, pp.215–224.

12.

Wang

Liu

Jolfaei

Resource allocation solution for sensor networks using improved chaotic firefly algorithm in IoT environment. Comput Commun 2020; 156: 91–100.

13.

Moraes

REN

Dos Reis

WWF

Rocha

HRO

, et al. Power-efficient and interference-free link scheduling algorithms for connected wireless sensor networks. Wirel Netw 2020; 26: 3099–3118.

14.

Kim

Wang

Park

, et al. A heuristic resource scheduling scheme in time-constrained networks. Comput Electr Eng 2016; 54: 1–15.

15.

Danmanee

Nakorn

Rojviboonchai

CU-MAC: a duty-cycle MAC protocol for Internet of things in wireless sensor networks. ECTI Trans Electr Eng Electron Commun 2018; 16(2): 30–43.

16.

Lee

Youn

. Group-based transmission scheduling scheme for building LoRa-based massive IoT. In: Proceedings of the 2020 international conference on artificial intelligence in information and communication (ICAIIC), Fukuoka, Japan, 19–21 February 2020, pp.583–586. New York: IEEE.

17.

Yang

Wang

Optimization of time and power resources allocation in communication systems under the industrial Internet of things. IEEE Access 2020; 8: 140392–140398.

18.

Liu

Gao

Optimal time scheduling scheme for wireless powered ambient backscatter communications in IoT networks. IEEE Internet Things 2019; 6(2): 2264–2272.

19.

Abdullah

Asghar

Ashraf

, et al. An energy-efficient message scheduling algorithm with joint routing mechanism at network layer in Internet of things environment. Wireless Pers Commun 2020; 111(3): 1821–1835.

20.

Gazori

Rahbari

Nickray

Saving time and cost on the scheduling of fog-based IoT applications using deep reinforcement learning approach. Future Gener Comp Sy 2019; 110(10): 1098–1115.

21.

Samanta

Tang

DYME: dynamic microservice scheduling in edge computing enabled IoT. IEEE Internet Things 2020; 7: 6164–6174.

22.

Olatinwo

Joubert

TH.

Energy efficiency maximization in a wireless powered IoT sensor network for water quality monitoring. Comput Netw 2020; 176: 107237.

23.

Qin

Chen

Cao

, et al. DIPS: dual-interface dual-pipeline scheduling for energy-efficient multihop communications in IoT. IEEE Internet Things 2019; 6(1): 718–733.

24.

Gabale

Chebrolu

Raman

, et al. PIP: a multichannel, TDMA-based MAC for efficient and scalable bulk transfer in sensor networks. ACM T Sensor Network 2012; 8(4): 28.

25.

Liu

Guan

Time slots allocating and multicycle scheduling in IWSN for narrow process automation. Int J Distrib Sens N. Epub ahead of print 18 June 2014. DOI: 10.1155/2014/324045.

26.

Lee

Cho

SH.

Tree TDMA MAC algorithm using time and frequency slot allocations in tree-based WSNs. Wireless Pers Commun 2017; 95(3): 2575–2597.

27.

Osamy

El-Sawy

Khedr

AM.

Effective TDMA scheduling for tree-based data collection using genetic algorithm in wireless sensor networks. Peer Peer Netw Appl 2020; 13: 796–815.

28.

Sgora

Vergados

DD.

A survey of TDMA scheduling schemes in wireless multihop networks. ACM Comput Surv 2015; 47(3): 1–39.

29.

Zhao

, et al. Distributed real-time data aggregation scheduling in duty-cycled multi-hop sensor networks. In: Proceedings of the international conference on wireless algorithms, systems, and applications, Honolulu, HI, 24–26 June 2019, pp.432–444. Cham: Springer.

30.

Tan

Wang

Xin

, et al. A multi-channel transmission scheme in green Internet of things for underground mining safety warning. IEEE Access 2019; 8: 775–788.

31.

Zhang

Luo

Cheng

, et al. CRTRA: coloring route-tree based resource allocation algorithm for industrial wireless sensor networks. In: Proceedings of the 2012 IEEE wireless communications and networking conference (WCNC), Paris, 1–4 April 2012, pp.1870–1875. New York: IEEE.

32.

Gao

Zhao

, et al. Edge-computing-based channel allocation for deadline-driven IoT networks. IEEE T Ind Inform 2020; 16(10): 6693–6702.

33.

Delicato

Pires

, et al. Efficient allocation of resources in multiple heterogeneous wireless sensor networks. J Parallel Distr Com 2014; 74(1): 1775–1788.

34.

Alam

Mehmood

Katib

, et al. Data fusion and IoT for smart ubiquitous environments: a survey. IEEE Access 2017; 5: 9533–9554.

35.

Jiang

Cao

, et al. Fairness-based packing of industrial IoT data in permissioned blockchains. IEEE T Ind Inform. Epub ahead of print 21 December 2020. DOI: 10.1109/TII.2020.3046129.

Resource scheduling based on routing tree and detection matrix for Internet of things

Abstract

Keywords

Introduction

Related work

System model

Network model

Transmission interference model

Symbolic representation

Proposed algorithm

TSRT

RSRM

Examples of RSRM

Theoretical analysis

Theorem 1

Proof

Theorem 2

Proof

Experiments

Conclusion

Footnotes

Declaration of conflicting interests

Funding

ORCID iDs

References