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Abstract

Recently, wireless sensor networks have employed the concept of mobile agent to reduce energy consumption and obtain effective data gathering. Typically, in data gathering based on mobile agent, it is an important and essential step to find out the optimal itinerary planning for the mobile agent. However, single-agent itinerary planning suffers from two primary disadvantages: task delay and large size of mobile agent as the scale of the network is expanded. Thus, using multi-agent itinerary planning overcomes the drawbacks of single-agent itinerary planning. Despite the advantages of multi-agent itinerary planning, finding the optimal number of distributed mobile agents, source nodes grouping, and optimal itinerary of each mobile agent for simultaneous data gathering are still regarded as critical issues in wireless sensor network. Therefore, in this article, the existing algorithms that have been identified in the literature to address the above issues are reviewed. The review shows that most of the algorithms used one parameter to find the optimal number of mobile agents in multi-agent itinerary planning without utilizing other parameters. More importantly, the review showed that theses algorithms did not take into account the security of the data gathered by the mobile agent. Accordingly, we indicated the limitations of each proposed algorithm and new directions are provided for future research.

Keywords

Wireless sensor network data gathering mobile agent multi-agent itinerary planning

Introduction

The emergence of wireless sensor networks (WSNs) has attracted much research interest and has become an active research area in a broad range of critical applications. WSN is the deployment of a vast number of sensor nodes, deployed in a field of interest to monitor physical or environmental conditions such as temperature, humidity, and velocity. Each sensor node consists of four main components: radio, a processor, sensors, and an energy source like a battery.¹ The sensor battery is finite in energy, and in some applications, it is not possible to replace or recharge the battery due to unreachable human environments. Therefore, managing the power consumption of sensor nodes in WSNs is an issue of utmost importance. An efficient power consumption among nodes leads to a prolonged lifetime of the whole network. The lifespan of an energy constrained node is determined by how fast the sensor node consumes energy. The consumption of energy in a WSN occurs due to several factors such as communication, data flow traffic, and data gathering operation.

In any application, the primary purpose of these sensor nodes is to sense and transmit the data periodically to the base station (sink) and then send it to users located at a remote site. In WSNs, sensor nodes cannot transmit their data directly to the sink individually since some sensor nodes are located far away from the sink. If the data are directly transmitted by far nodes to the sink, these nodes will die much earlier in comparison to other sensor nodes that are closer to the sink due to limited energy. Therefore, each sensor node has to transmit its data to other neighbor nodes via multi-hop until it reaches the sink. This process of sampling the information and transmitting data from nodes to the sink is called data gathering,² which is considered as one of the challenges in designing a WSN.³

Many researchers have widely pursued data gathering to minimize the power consumption in WSNs. Over the years, protocols such as LEACH, PEGASIS, and PEDAP^4–6 have been proposed to minimize energy consumption and increase the lifetime of sensor nodes. However, balancing the amount of data among an enormous number of nodes has become a challenging issue which leads to data congestion, increased latency, and high energy consumption. This has proved that data transmission consumes much energy than data processing.⁶ Sending a single bit can consume the same energy as executing 1000 instructions at typical sensor node.⁷ Therefore, it will be more energy efficient if the nodes keep its data in its memory and waits for an autonomous mobile computational code to gather the data. To mitigate such problems, researchers have proposed the use of mobile agent (MA) as an efficient approach for data gathering in WSNs to minimize energy consumption and to prolong the network lifetime.^8–11

In WSNs, MA can be defined as a packet¹¹ that carries a computational code with an assigned itinerary (the route that the MA should follow). The sink dispatches the MA that visits the nodes one by one to do a particular task. In WSNs, MA has been used in various environments for different tasks such as data fusion¹² and data gathering.¹³

The use of MA in WSNs has various applications.¹⁴ Some of these applications include, but not limited to, image querying,¹⁵ target tracking,¹⁶ and searching for disaster victims.¹⁷

Image querying application (visual sensor networks). In this application, the MA is dispatched to the target region carrying a specific image segmentation code. The task of the MA here is to visit the image sensors one by one and reduce the large volume of imagery data at each sensor node by the carried image segmentation code. Thus, instead of transmitting the very large amount of data generated by an image sensor to the sink (which consumes much bandwidth and energy), the MA performs a local image segmentation process at each visited sensor node.

Target tracking application. In this application, the MA is dispatched to track the traveling path of a new detected target. Using the signal energy measurement, the closer the target to the node, the stronger the signal energy. After the MA is dispatched, it continuously gathers new information if migrated to the nodes with stronger signal energy and progressively increases the precision of detecting the target. Once the MA archives a certain precision threshold, it terminates the tracking task and then returns to the sink node with the information collected.

Disaster victims application. In this application, the MA is dispatched by unmanned aerial vehicle (UAV) to gather the information about the disaster victims in an unreachable place using landed vehicles. The UAV drops light sensor nodes in a disaster place (such as earthquake), and then these nodes communicate with the victims via their cell phones by save our souls (SOS) signals. When the UAV flies over the sensor nodes, the UAV dispatches the MA to roam and gather the data from sensor nodes. After the MA completes the gathering task, it will return back to the UAV with the location information about the victims.

The use of MA to perform data gathering in WSNs can be performed by two itinerary planning: single-agent itinerary planning (SIP) and multi-agent itinerary planning (MIP). In SIP, only one MA migrates to the network, while in MIP, multi-agents are dispatched to the network and work in parallel. Although MIP overcomes the weakness of SIP, it suffers from problems such as determining the optimal number of MAs and their optimal itineraries. Therefore, in this article, we reviewed the existing algorithms that have been identified in the literature to find out the optimal number of MAs in MIP. Additionally, we highlighted the limitations of each proposed algorithm to provide researchers with research directions.

The remainder of this article is structured as follows. Data gathering models in WSNs are introduced in “Data gathering models in WSNs,” while section “MA itineraries in WSNs” presents MA itineraries types. In section “MA itinerary planning,” the two MA itinerary planning such as SIP and MIP are discussed, and then most of the proposed algorithms of determination of optimal number of MAs are described in section “Determination of optimal number of MAs in MIP.” Discussion and future research directions are presented in section “Discussion and future research directions,” whereas section “Conclusion” concludes this article.

Data gathering models in WSNs

In this section, data gathering models in WSNs are classified. In WSNs, data gathering process can be performed by different models. Figure 1 shows the taxonomy of data gathering models in WSNs, which is divided into two main models namely, client–server model and mobility model. A discussion regarding each sub-model can be found in the following sections.

Figure 1.

Taxonomy of data gathering models in WSNs.

Data gathering based on client–server model

The primary goal of WSNs is to collect and route the sensed data from nodes to the sink or base station for processing purpose. In WSNs, the traditional approach of data delivery contains multi-hop communication among sensor nodes until it reaches the sink (which is a static node). Figure 2 shows that in client–server-based paradigm, the sensed data are transmitted from nodes to the sink individually. The nodes closer to the sink receive and send more data on behalf of other nodes, and it may run out of energy before the other nodes.¹⁵ Thus, this could lead to unbalanced energy consumption. Transmitting large data also incurs much network traffic which in turn causes delay due to the shared bandwidth. Overall, the paradigm leads to high bandwidth and energy consumption since the number of data flows is normally equal to the number of the nodes in the network. Therefore, to respond to the above drawbacks of client–server model, the mobility model of data gathering was proposed. This model decreased the high bandwidth by moving the processing unit in a mobility manner and the data gathering is done at the node itself.

Figure 2.

WSNs data gathering based client–server model.

Data gathering based on mobility model

With the mobility model, data gathering in WSNs has been improved with efficient energy consumption. Strategies employed for data gathering in mobility model are as follows: mobile sink, a mobile node, and mobile software agent. In mobile sink and mobile node data gathering strategy, the sink or node is allowed to roam the network for data collection from various sources, while in mobile software agent strategy, only the software is migrated through different sources for data collection. We further elaborate on each of these strategies in the following sections.

Mobile sink

Mobile sink model was one of the proposed solutions for data gathering.^18,19 In this strategy, the sink is allowed to collect data from nodes while roaming the network.¹⁹. One or multiple mobile sinks can be used to travel throughout the network to gather the data from source nodes.¹⁸ Although this strategy achieved better data gathering with efficient energy consumption, it has some drawbacks such as sink trajectory and velocity. Another challenge here is the tradeoff between controlling the mobile sink node data gathering and satisfying the quality of service (QoS) under the energy constraint.²⁰ Moreover, these challenges of mobility hardware limit the application of WSNs, which is not applicable in harsh environments.

Mobile node

In recent data gathering approaches, the mobile node (or relocatable nodes) data gathering strategy is employed. These mobile nodes change their location in order to relay or forward the data from the source nodes to sink. Thus, compared with the mobile sink, mobile nodes do not gather data when they roam in the network, they only act as connectors to change the topology of the network to get better link connections among the nodes.¹⁸ This strategy relieves the relaying overhead of sensor nodes located close to the sink which suffer from the hot-spot problem. It also mitigates the connectivity issue as nodes no longer need to establish and maintain a static connection among them.^21,22 However, finding the optimal number of mobile nodes as well as controlling the speed of them is one of the challenges of this approach.²¹

Mobile software agent

The emergence of MA in WSNs has alleviated the constraints mentioned above.²² MA carries the processing function as a small code inside a packet sent from node to node. At each node, this code then executes itself locally to perform data gathering, thus achieving a computational flexibility in WSNs in contrast to the client–server model.¹⁴ This feature, in addition to autonomous, interactive, and intelligence, has aided the reduction in the cost of energy consumption and communication²³ as well as the probability of transmission error and collision. As shown in Figure 3, the MA follows an assigned itinerary to visit the nodes sequentially. The sink determines this itinerary (details in section “MA itinerary planning”). An MA itinerary is the route that the MA should follow.

Figure 3.

WSNs data gathering based on mobile agent.

In some applications, where sensor nodes generate a large amount of sensory data, the MA visits the sensor nodes and performs a local data reduction process at each source node. This local reduction process is used to eliminate the redundant sensed data where the nodes are closely located (density deployment). After this process, a data aggregation function is needed to fuse the reduced data at each source node in a small size packet. As presented in Chen et al.,¹⁵ the size of the reduced data at source i by the MA-assisted local reduction process can be calculated using equation (1)

$R_{i} = S_{data}^{i} \cdot (1 - r)$ (1)

where $R_{i}$ is the data reduced at source i, $S_{data}^{i}$ is the size of raw data at source i, and r is the reduction ratio (0 < r < 1).

After the MA completes the reduction process at source i, it migrates to the next source node (i + 1) to perform the same reduction process and then aggregates the result with the one that already carried from source i. Therefore, the size of accumulated data after the MA leave source i can be calculated by equation (2)

$\begin{matrix} S_{ma}^{1} = R_{1}, \\ S_{ma}^{2} = R_{1} + (1 - P) \cdot R_{2} \\ S_{ma}^{i} = R_{i} + (1 - P) \cdot R_{i} \\ = R_{1} + \sum_{k = 2}^{i} (1 - P) \cdot R_{k} \end{matrix}$ (2)

where $S_{ma}^{i}$ is the size of the accumulated data after the MA leaves source i, P is the aggregation ratio (0 ≤ P ≤ 1), and $R_{i}$ is the amount of data aggregated by P.

Note that in equation (2), there is no data aggregation at the first source node. The value of P can be varied from an application to another.

MA itineraries in WSNs

In this section, we discuss the types of MA itinerary. MA itinerary is the route that MA should follow to visit the nodes.²⁴ In MA-paradigm-based WSN, there are two types of MA itinerary: static and dynamic. These types of MA itineraries can be determined based on the decision of next node’s migration.²⁵

Static MA itinerary

In static itinerary, the dispatcher node (i.e. sink node) computes the itinerary of the MA before the MA migrates to the network. Therefore, the MA has to carry a predetermined itinerary list for the order visiting nodes. In Qi and Wang,²⁶ they present two static itinerary approaches: local closest first (LCF) and global closest first (GCF). In the LCF, MA starts its migration from the sink and looks up for the next hop with the shortest distance to the current node, while in the GCF, MA looks up for the next hop with the shortest distance to the sink. Static itinerary algorithms are more suitable for monitoring application such as measuring physical quantity.²⁷ However, the sink node is required to maintain the global information of a network topology to determine the MA itinerary; the sink considers this as an extra computational cost. Moreover, in the static itinerary, any node or link failures may invalidate the MA migration since it carries a predetermined itinerary list.²⁸

Dynamic MA itinerary

In dynamic itinerary, unlike static itinerary, the decision of next hop node of MA migration is taken at each hop, so the agent does not have to carry a predetermined itinerary list for decision-making. The MA that utilizes this type of itinerary is intelligent enough to learn certain changes (such as a new node joining the network or an existing node leaving the network) in network topology while continuing its tour for data gathering.²⁹ The dynamic approach is more appropriate for target tracking due to its zero dependence on a predetermined itinerary list as compared to the static approach. This independence makes it invulnerable to node and link failure.³⁰ However, a dynamic itinerary requires more time when the MA takes the next hop decision at each sensor node. Additionally, the more intelligence integrated within the MA, the larger its size. This will lead to consumes more processing energy at each node due to next hop decision.²⁷ It should be noted that in MA-based data gathering, majority of the MIP proposed approaches are static while in SIP, the dynamic itinerary approaches are widely used.

MA itinerary planning

Itinerary planning is the determination of the order of source nodes to be visited by the MA, which has significant effect on the energy performance of the network. The itinerary planning is classified into SIP and MIP. In SIP, only one MA is dispatched from the sink that visits the source nodes, whereas in MIP, several MAs are dispatched from the sink. However, finding the optimal itinerary planning of MA in a large-scale network is of vital importance to the network performance regarding energy efficiency and task duration.

It is noteworthy that the MIP is made up of two or more SIPs working concurrently to visit clusters of source nodes. The MIP algorithms were developed based on the SIP algorithms. Therefore, in order to have a good understanding of MIP, there is a need to first have a good grasp of the working process of SIP. Accordingly, an overview of the SIP is thus presented.

Single MA itinerary planning

Early literature of using MA in WSNs²⁶ presented two SIP approaches, namely, LCF and GCF. In LCF, MA migrates to the next hop with the shortest distance to the current node, while in GCF, MA migrates to the next hop with the closest distance to the center of the surveillance zone. Figure 4 shows the difference between LCF and GCF algorithms. In Chen et al.,⁸ MA-based directed diffusion (MADD) was proposed. MADD is similar to LCF but differs in which MA selects the node as the first source that has the farthest distance from the sink. Itinerary energy minimum for first-source-selection (IEMF) and itinerary energy minimum algorithm (IEMA) are two algorithms were proposed by Chen et al.²⁴ to achieve energy-efficient itineraries. In IEMF algorithm, MA chooses the first source node based on estimated communication cost which extends LCF. Moreover, the impact of data aggregation and energy efficiency are considered in IEMF to get an energy-efficient itinerary. The second algorithm IEMA—which is an iterative version of IEMF—selects an optimal source node as the next source based on estimated energy cost. However, all of the previous works do not perform well in large-scale sensor networks, and they suffer from several main drawbacks as described in Bendjima and Feham.³¹ The drawbacks include the following:

Long delays when single MA has to visit hundreds of sensor nodes.

Sensor nodes in the itinerary of the MA deplete energy faster than other nodes.

In SIP, the size of MA packet increases during the aggregation of data from node to node as shown in Figure 5. Moreover, increase in size of MA packet consumes higher energy especially when MA migrates from the last node to the sink.

Reliability reduces when the MA accumulates an increasing amount of data.

When the MA migrates to several source nodes, the chance of being lost increases.

Figure 4.

LCF algorithm and (b) GCF algorithm.

Figure 5.

Single mobile agent itinerary planning (SIP).

Multi-MA itinerary planning

In multi-MA itinerary, several MAs dispatched from the sink and worked in network parallel manner. Each MA follows its assigned itinerary and visits a subset of source nodes. In contrast to SIP, MIP overcomes the weaknesses of using SIP, especially on a massive scale of WSN.^32,33

Figure 6 shows that the multi-MAs are dispatched to the network area with two different itineraries. In MIP, dispatching multi-MAs decreases the packet size of each MA, which has been defined as one of the limitations in SIP. The decrease in the MA packet size is obtained due to the distribution of tasks that assign each MA to an individual itinerary. Additionally, when multi-MAs migrate to the network, each MA will visit a sequence of nodes (a group of nodes) and then minimize the task duration (lower delay).

Figure 6.

Multi-mobile agent itinerary planning (MIP).

Determination of optimal number of MAs in MIP

Determining the optimal number of MAs and their corresponding subsets of source nodes is a challenging issue. Figure 7 shows the determination of the optimal number of MAs in MIP which can be classified into two network topologies: homogeneous network with one sink and heterogeneous network with multiple sinks. Most of the existing MIP algorithms have proposed a homogeneous network with one sink located at the center of the network. Of recent MIP, a heterogeneous network with multiple sinks has been proposed by Gavalas et al.³⁴ In this article, the focus is on determining the optimal number of MAs in a homogeneous network topology with one sink. The existing algorithms reviewed include tree-based MIP, central location based MIP (CL-MIP), genetic algorithm based MIP (GA-MIP), directional angle based MIP, and greatest information in the greater memory based MIP (GIGM-MIP).

Figure 7.

Classification of determination of optimal number of MAs in MIP.

Tree-based MIP

In Mpitziopoulos et al.,³³ near-optimal itinerary design (NOID) algorithm was proposed to address the problem of calculating the number of near-optimal routes for MAs that incrementally fuse the data as they visit the nodes in a distributed sensor network. NOID algorithm adapts a method presented in Esau and Williams³⁵ namely the Esau–Williams heuristic that was designed for the constrained minimum spanning tree (CMST) problem in network designing. NOID algorithm iteratively groups the sensor nodes in the network to separate sub-trees that are connected progressively to the processing element (PE) or sink. Finally, each sub-tree is assigned to an individual MA.

Gavalas et al.³⁶, proposed another tree based algorithm named second near-optimal itinerary design (SNOID). This algorithm improves NOID algorithm by taking into account the nodes communication cost. SNOID determines the number of MAs and their itineraries they should follow by partitioning the area around the sink or PE into concentric zones (Figure 8). The number of nodes within the radius of the first zone includes the PE that represents the starting points of the itineraries of the MAs (or the number of MAs). The first zone radius can be calculated by $a r_{\max}$ , where a is an input parameter in the range [0, 1] and $r_{\max}$ is the maximum transmission range of any sensor node. The path of MAs itineraries starts from the inner (close to PE) zones and proceeds to outer zones.

Figure 8.

Partitioning the area around PE into concentric zones.³⁶

An improvement to the basic algorithms, NOID and SNOID, has been obtained by a tree-based itinerary design (TBID) algorithm presented in Konstantopoulos et al.³⁷ TBID not only finds the optimal number of MAs, but also creates low cost itineraries for each individual MA. TBID can be suitable for WSNs with dynamic network conditions due to its low computational complexity.

Gavalas et al.³⁸ introduced a novel algorithm for energy-efficient itinerary planning of MAs. This algorithm adopts a meta-heuristic method called iterated local search (ILS) to derive the hop sequence of multiple traveling MAs over the deployed source nodes. Like other tree-based MIP algorithms (e.g. NOID and TBID), ILS is executed at the sink and determine the number of itineraries (MAs) by considering a circular zone around the sink. The nodes that are lying in the sink zone will be the start points of each MA itinerary. However, the difference from other previous tree-based MIP algorithms is that ILS algorithm considers the increasing MA size as well as the energy spent for migrating to intermediate nodes along its itinerary.

Although NOID, SNOID, TBID, and ILS perform better than LCF and GCF, the MA in these algorithms (tree-based algorithms) consumes twice as much energy due to the reverse routes that the MA take, especially when there are huge amount of branches. Moreover, since the itinerary of the MA is predetermined at the PE (sink), any change in the network topology such as a node and link failures may invalidate the migration of MA.

CL-MIP

CL-MIP is another algorithm proposed by Chen et al.³⁹ to determine the proper number of MAs. The author presented an algorithm to create MIP solutions. The main idea of the CL-MIP is to consider the solution of MIP as an iterative version of the solution of SIP. CL-MIP algorithm includes the following four parts:

Visiting central location (VCL) selection algorithm;

Source grouping algorithm for each MA;

Determining the source-visiting sequence using SIP algorithm;

An iterative algorithm to ensure that a MA has covered all the source nodes.

In CL-MIP, VCL algorithm is used to group all the nodes of origin according to the node density (gravity algorithm).³⁹ The basic idea of VCL algorithm is to distribute each source nodes impact factor to other source nodes. Let n represent the source node number; then each source node will receive (n − 1) impact factors from other source nodes, and one from itself. After calculating the accumulated impact factor, the location of the source with the largest accumulated impact factor will be selected as a VCL. Then, the source nodes within the radius of VCL will be assigned to the MA. The above process will repeat until all the remaining source nodes are assigned to an MA. Finally, the itinerary for each MA can be planned by any SIP algorithms such as LCF, GCF, and IEMF. However, VCL algorithm assumes that the relevant source nodes are arranged geographically distributed in several clusters, which limits the use of the algorithm in a broad range of applications.

GA-MIP

A GA-MIP was proposed in Cai et al.⁴⁰ to find the optimal number of MAs to MIP. In Figure 9, GA-MIP is about gene that consists of source-ordering-code (sequence array) and source grouping code (group array). A source-ordering-code is an array that includes segments; each segment has number of source nodes to be visited by a particular MA. While source grouping code is an array of numbers, with each number specifying the number of source nodes of each segment in the source-ordering-code. The results show that the proposed GA-MIP has better performance regarding the issues of delay and energy consumption. However, this greedy approach may lead to a substantially sub-optimal MIP solution and high computation complexity.

Figure 9.

GA-MIP algorithm.⁴⁰

Directional angle based-MIP

In this algorithm, an angle gap based MIP (AG-MIP) is used for grouping all the source nodes in a particular direction as a single group.⁴¹ The main idea of direction-based MIP is to establish AG-MIP to divide the network into sectors as shown in Figure 10. A particular angle gap threshold determines each sector. Then, all nodes around one central location (VCL) within this sector must be included in the same group. Therefore, the source grouping algorithm is direction oriented. The two nodes with minimal angel gap determine the VCL here, which differs from the previous algorithm of VCL that presented in section “CL-MIP.”

Figure 10.

Angle gap grouping results.⁴¹

As a comparison with VCL, direction-based MIP more efficiently groups the source nodes, but this algorithm may result in few isolated source nodes that are located near the group. These isolated source nodes will finally be considered as a new sector after several iterations. Moreover, how to find an optimal angle gap threshold in this approach is still an open issue.

Wang et al.⁴² improve the previous work presented in Cai et al.⁴¹ by proposing an algorithm entitled directional source grouping based MIP (DSG-MIP). This algorithm partitions the network area into sector zones whose centers are the sensor nodes within the radius of the sink node or PE. Figure 11 shows that the size of the PE zone can be determined by the same algorithm presented in SNOID algorithm, $a R_{\max}$ where R is the maximum transmission range, and a is an input parameter in the range [0, 1]. Then, the sensor node within this zone represents the starting points of each MA. By controlling the value of parameter a, the number of MAs can be determined. As shown in Figure 11, after three iterations, there are only two isolated source nodes remaining, u and v. These isolated source nodes (u and v) are simply grouped and assigned to the last itinerary with node f as the starting point. The contribution of DSG-MIP was that those isolated source nodes can be inserted into existing itineraries one by one according to the metric of shortest distance to the itinerary. Then, the incremental cost of the formed itinerary is minimized. However, inserting the isolated source nodes into existing itineraries could increase the delay of the MA especially when the isolated source nodes are located far away from the existing itineraries. Moreover, similar to AG-MIP algorithm, DSG-MIP algorithm is unable to find the optimal gap threshold.

Figure 11.

Directional source grouping algorithm (DSG-MIP).⁴²

Greatest information in the GIGM-MIP

In the previous algorithms of determining the optimal number of MAs, most of the itinerary planning algorithms are based only on geographic information. The author in Aloui et al.⁴³ proposed a new MIP algorithm called GIGM-MIP to determine the number of MAs with their source nodes grouping. This algorithm is based not only on geographic information, but also on the amount of data provided by each source node. GIGM-MIP algorithm is divided into three parts: (1) Partitioning the network into a set of partitions based on geographical information and each partition can have several MAs. (2) Finding out the necessary number of MAs and their groups of nodes while considering the data size provided by each source node. (3) Defining the itinerary plan for each MA to visit the source nodes. As shown in Figure 12, the network is partitioned into two partitions, and one of the partitions has more than one MA.

Figure 12.

Partitioning the network by GIGM-MIP algorithm.⁴³

Partitioning the network in GIGM-MIP algorithm is established according to the distance among the sensor nodes (nodes closest to each other are grouped together). K-Means algorithm is used to partition the network into K clusters. However, although K-Means is an efficient algorithm for a large-scale network, some clusters K must be specified. In MIP, the number of clusters has to be determined optimally according to several parameters such as the distance between nodes, density, and energy of nodes.

Discussion and future research directions

The use of MIP for data gathering purpose in WSNs achieves a significant improvement in minimizing the energy consumption and thus prolongs the lifetime of the network. By grouping the sensor nodes into several groups (partitions), MIP decreases the MA packet size by visiting a group of sensor nodes individually. Furthermore, due to the distribution of the given tasks, the task duration is decreased when MIP is applied. However, with these advantages of MIP, grouping the sensor nodes and finding the optimal itinerary of each MA to visit the given set of the sensor nodes is a challenging issue. In section “Determination of optimal number of MAs in MIP,” the reviewed approaches have proposed different algorithms to find an optimal grouping of the sensor nodes. Table 1 compares the proposed algorithms in terms of the parameters that were used to find the optimal grouping of the sensor nodes. Most of the MIP^39,41,42 algorithms used the nodes density as the main factor to group the visiting nodes, while other algorithms used different parameters such as nodes radius and communication cost.^33,36,37 In Aloui et al.,⁴³ the number of groups (partitions) is manually specified, but the number of MAs is determined by the data size in each partition; therefore, each partition may have several MAs. However, the optimal partitioning of the network has to take into account several parameters such as density, communication cost, energy, and data size at each sensor.

Table 1.

A comparison of MIP algorithms in terms of parameters used to find the optimal number of partitions and MAs.

Algorithm	Number of partitions is determined by	Parameters used for partitioning	Number of MAs is determined by	Itinerary planning of MA
NOID³³	Number of nodes within the radius of the PE	Node radius	Number of partitions	Static
SNOID,³⁶ TBID,³⁷ and ILS³⁸	Number of nodes within the radius of the PE	Node radius and communication cost	Number of partitions	Static
CL-MIP,³⁹ AG-MIP,⁴¹ and DSG-MIP⁴²	Number of VCLs	Nodes density	Number of partitions	Static
GA-MIP⁴⁰	An initial number of MAs	Distance between the sensor nodes	Optimal number of MAs	Static
GIGM-MIP⁴³	Number of partitions is manually specified	Distance between the sensor nodes	Number of MAs is determined by the data size in each partition	Static

MIP: multi-agent itinerary planning; MA: mobile agent; GA-MIP: genetic algorithm based MIP; GIGM-MIP: greater memory based MIP; CL-MIP: central location based MIP; AG-MIP: angle gap based MIP; DSG-MIP: directional source grouping based MIP; ILS: iterated local search; TBID: tree-based itinerary design; SNOID: second near-optimal itinerary design; NOID: near-optimal itinerary design; VCLs: visiting central locations.

Based on what is mentioned above, some future research directions are highlighted as follows.

Efficient source nodes grouping of MIP

Grouping the source nodes is the key challenge in MIP. An effective algorithm for source nodes grouping will result in efficient energy consumption. The previous algorithms of grouping the source nodes that were reviewed have some weaknesses. Therefore, it would be interesting on how to find out a way of group the source nodes efficiently. X-Means algorithm presented in Pelleg and Moore⁴⁴ could be suitable to produce an efficient source nodes grouping. In K-Means algorithm presented in Aloui et al.,⁴³ the number of groups (clusters) has to be specified manually by the user where in X-Means algorithm, the number of groups, is optimally obtained.

Dynamic itinerary of MIP

In MIP planning algorithm, most of the proposed solutions assume that the itinerary of each MA is determined at the sink node, which means the MA is carrying a static itinerary. In this case, any change in the network topology due to node mobility or node failures (such as energy depletion) could affect the migration of MA. The migration of MA has to be dynamic and more intelligent, such that the MA migration is decided at each visited sensor node. Therefore, it is recommended that the MA packet carries an alternative source nodes list together with the list that is predetermined at the sink. The alternative source nodes list will contain the nearest neighbor node of each next hop node. This proposed solution might increase the MA packet size slightly. The added alternative source nodes list (to the MA packet) could increase the time of MA hop migration at each node. While this solution consumes energy and time, on the other hand, however, it is beneficial and applicable for dynamic migration (such as target tracking applications).

Collaboration of multi-MAs in MIP

As long as several MAs are dispatched and work in parallel for data gathering in MIP, each MA assigned to individual data gathering task. It is recommended that each MA collaborates with other MAs to distribute the assigned tasks. In the previous MIP algorithms, each individual MA itinerary has its own source nodes list and the number of source nodes of each MA itinerary is varied from one to another. Moreover, each MA starts its migration from the sink and returns back again to the sink. For instance, in Figure 12, one of the clusters has two itineraries and one of these itineraries has fewer source nodes than the second one. From this point, it is suggested that such source nodes should collaborate with other MAs that have more source nodes to visit. This collaboration could decrease the overall task duration of MAs and balance the data size carried among all MAs. Thus, a high QoS will be provided while taking into account the task duration and energy consumption.

MA data security

The data carried by the MA are assumed to be secure with the MA migration. Since the migration of the MA is done by several hops among the sensor nodes, the limited available energy at these sensor nodes will affect the MA migration and the data carried by the MA may be lost. Therefore, it is recommended to use any of the compression algorithms to compress the data accumulated by each MA. The compression code with an encryption key should be carried by the MA so that once the MA reaches the source node, it compresses the accumulated data and then later when the MA finishes its task, the encrypted data accumulated will be decrypted at the sink.

Conclusion

In this article, we analyzed the background of data gathering in WSNs using MA-based model. The main goal of this article was to show the impact of using MIP for data gathering in WSNs. It has been proven that using MIP for data gathering achieves a significant improvement in minimizing the energy consumption. MIP overcomes the weakness of SIP in terms of task duration and MA packet size, but on other hand, MIP still has some drawbacks. In general, it seems that finding the optimal number of MAs in MIP is considered as a non-deterministic polynomial (NP)-hard problem. Therefore, this article reviewed and discussed the existing algorithms that have identified in the literature to determine the optimal number of MAs in MIP. Particularly, we analyzed the most adapted algorithms: tree-based MIP, CL-MIP, GA-MIP, directional angle based MIP, and GIGM-MIP. This article showed that most of the algorithms used one parameter to find the optimal number of MAs in MIP without utilizing other parameters which could give efficient results. More significantly, this article demonstrated that these algorithms have not considered the security of the data gathered by the MA.

Consequently, the limitations of each proposed algorithm were shown and new directions are provided for future research. In particular, we have started working on the two of the proposed approaches: efficient source nodes grouping of MIP (with X-means algorithm) and collaborative of multi-MAs in MIP. The results are promising and will be presented in future articles.

Footnotes

Academic Editor: Gour C Karmakar

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) received no financial support for the research,authorship,and/or publication of this article.

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