2.1 Introduction

In recent research into Wireless Sensor Networks (WSNs), trust-aware data aggregation using a mobile agent is the new challenge. This network is made up of small sensor nodes, which contribute to the communication of data [1]. WSN is application specific, therefore, it has the ability to deploy thousands of small sensor nodes, which can assemble themselves and are capable of collecting data from their surroundings and route the data to the sink node by either single hops or multiple hops [2]. Sensor nodes constitute different units, known as the communication unit, sensor unit, processor unit, and energy unit, as shown in Figure 2.1. Sensor units collect data from their surroundings and send them to the processor unit. In the processor unit, data are processed and saved for future use. The processed data from the processor unit are then forwarded to the communication unit, which is accountable for communicating the particulars from node to node. The main job of the energy unit is to keep a check on residual energy in the nodes. A sensor node has limited onboard storage, processing, power, and radio capacities, due to its limited size. Therefore, WSNs necessitate efficacious mechanisms to utilize limited resources and to resolve associated problems. Routing is one of these mechanisms that elongates the network lifespan by lessening energy consumption in communication. Figure 2.1 shows the architecture of the sensor node.

WSN is widely used in many fields, such as precision agriculture, environmental applications, smart transportation, health care applications, smart buildings, military applications, and many more. When a sensor network is used for military applications, continuous monitoring is essential to keep a check on movement of the enemy “like” mass destruction security assistance, etc. WSN is also very useful in health applications as it can remotely monitor the patients and checks their heart rate and blood pressure, transmitting this information to a WSN doctor. Table 2.1 summarises the applications of WSN.

TABLE 2.1
Summary of Various WSN Applications with Their Characteristics

Energy saving is a challenging task for researchers, as more energy is consumed during data transmission. The consumption of energy also depends on other factors, like the redundancy of transmitted data, traffic flow of data packets, and communication bandwidth [18]. In WSNs, sensor nodes are not able to send data directly to the sink node, as fewer nodes lie at some distance from the sink. If they do so, then nodes which lie far away from the sink will die out earlier, as compared with the other nodes. Therefore, multi-hop communication is used, where all nodes send their data to their next-nearest node till they reach the sink node. The process of collecting and transmitting data from the nodes to the sink node is called the data aggregation process. Data aggregation is an important process, because its aims are to collate data from different sources. Different approaches for data aggregation include tree-based, multipath, cluster-based, hybrid, centralized, and in-network data aggregation approaches, which are studied with respect to different performance parameters, such as energy efficiency, data accuracy, latency, overhead, network lifetime, and scalability of the sensor network.

Research has pursued data aggregation techniques to lower the energy consumption in WSNs. In the past few years, protocols like Low-Energy Adaptive Clustering Hierarchy (LEACH), Power-Efficient Gathering in Sensor Information System (PEGASIS) and many more have been proposed by researchers to boost energy efficiency and to extend the network lifetime. For increasing the scalability of networks efficiently, hierarchical architecture is the preferred one. In this protocol, the holistic network is divided into layers, with nodes of an individual layer playing the same role. Clustering-based protocols are preferred, because they are energy-efficient methods. In clustering, there are two main parts: cluster members and a cluster head (CH). The selection of the CH depends on the estimated energy left in the node. The main responsibility of a CH is to remove redundancy and make transmission successful by reducing data traffic. The CH aggregates data from its cluster members and transmits it to the base station (BS). Thus, the process of clustering is divided into three different steps: selection of CH, formation of cluster, and data transmission.

The main drawback of clustering is the random distribution of the CH and the greater energy consumption by the CH, using single-hop communication. In addition, unequal load balancing occurs during data transmission in multi-hop communication with the BS. An alternative approach to gathering information from the nodes of network is with the help of a mobile agent (MA). Data aggregations with the help of MAs satisfy the requirement of energy efficiency and network lifetime. MA is a self-adaptive type of middleware technology, which moves autonomously in a network without any pre-installed requirements.

The use of MA in the context of WSN has various applications, like data aggregation, target tracking, reprogramming, healthcare, image querying, and intrusion detection, etc. When the MA is dispatched from the BS, it follows one of two itineraries, namely single itinerary planning (SIP) or multi-itinerary planning (MIP). Different algorithms of SIP have been compared and their findings and disadvantages will be discussed in this chapter. Various MIP algorithms, which describe the number of MAs required on the basis of partition, will also be discussed.

Further research has been pursued on external and internal attacks. Two principal methods of providing security in WSN have been proposed, namely cryptography and intrusion detection system (IDS). Cryptography is well suited to foreign attacks, but it fails to manage domestic attacks. IDSs are used to reveal malicious nodes in the network. IDS is a complex process, which follows a certain set of norms, with which the results should be examined. Thus, to ensure the security of WSN, trust-aware management has been introduced over the past few years. It calculates the value of trust of the nodes, which is further used in different processes of WSN, such as data aggregation and data routing. A detailed study of trust-aware management is discussed in the following sections.

The remaining sections of this chapter are structured as follows. A detailed survey of routing protocols is described under Section 2.2. Various data aggregation and clustering techniques are described in Sections 2.3 and 2.4. The drawbacks of clustering and the introduction of MA, along with their itineraries, are given in Sections 2.5 and 2.6. The importance of trust in WSN is discussed in Section 2.7. In the Conclusions, different research strategies are compared and discussed.

2.2 Routing Protocols in WSN

Different routing protocols are described in this section. Routing is the foremost parameter in the design of WSN. Researchers classify routing protocols into four different categories: network structure, communication model, topology-based protocols, and reliable routing protocols. Figure 2.2 shows the detailed taxonomy of routing protocols [19].

2.2.1 Network Structure

The network structure can be defined in accordance with the distribution of the sensor nodes. It can also be defined on the basis of the type of connection between the nodes, and by the routing method used to transfer the information. It can be classified as follows:

1. a) Flat Routing Protocols. In flat-based protocols, all nodes of the sensor network are at the same level within it. It is a multi-hop routing technique [20] (Table 2.2).
2. b) Hierarchical Routing Protocols. The whole network is divided into a small group of nodes called clusters, in which the CH is chosen, depending on the residual energy. When clusters are created in a sensor network, it results in an extended network lifetime, and improved energy efficiency and stability [31]. Different hierarchical routing protocols have been studied, and they are compared with respect to different parameters in Table 2.3.

TABLE 2.2
Comparison of Various Flat Routing Protocols

Routing Protocol	Classification	Data Aggregation
SPIN [21]	Negotiation	Yes
DD [22]	Data-centric	Yes
Rumour routing [23]	Data-centric	No
Gradient-based routing [24]	Data-centric	Yes
Constrained Anisotropic Diffusion Routing (CADR) [25]	Data-centric	No
Energy-Aware Routing (EAR) [26]	Data-centric	Yes
Minimum Cost-Forwarding Algorithm (MCFA) [27]	Data-centric	No
Active Query-Forwarding In-Sensor network (ACQUIRE) [28]	Data-centric	Yes
Sequential Assignment Routing (SAR) [29]	QoS-based	Yes
Energy-Aware Data-Centric Routing (EAD) [30]	Data-centric	Yes

 

TABLE 2.3
Comparison between Hierarchical Routing Protocols

 

2.3 Data Aggregation Approaches

Energy utilisation is the prime concern in sensor networks, as sensor nodes cannot send data to the BS directly. Sometimes, redundancy is increased by the neighbouring sensor nodes, which results in congestion at the BS, which becomes loaded with enormous amounts of data. As a consequence, it is necessary that redundancy is removed at some intermediary levels, which can result in a reduction in the number of data packets, which are forwarded to the BS. Reducing traffic at the BS saves energy and bandwidth. This approach is known as data aggregation [45]. It increases the lifetime of the network by decreasing the total data transmissions. The multi-hop approach is used to forward the sensed data to the CH. The CH uses either a traditional wired network or a fixed wireless medium to deliver the aggregated data to the BS. There are various approaches to data aggregation: the tree-based approach, centralised approach, in-network approach, cluster-based approach, and the multi-path approach.. Figure 2.3 describes the various data aggregation approaches.

Clustering is the most efficient data aggregation approach. It extends the network lifetime by reducing energy consumption. With the help of clustering, direct interaction among sensor nodes and the BS is decreased, which results in removed redundancy and reduced energy consumption.

2.4 Clustering in WSN

WSN is application oriented but, still, these networks suffer from several restrictions like energy constraints, limited battery capacity, etc. Nodes in the sensor network have less power and range, so cannot communicate directly with a sink node, if it is located at some distance away. If a sensor node tends to communicate directly with a distant sink node, it will give rise to greater use of energy resources, which results in network failure. Thus, to boost the lifetime of the network, the clustering technique is used. The selection of CH depends on the energy resources and it coordinates activities inside and outside the cluster. It also removes redundancy from the data gathered from other cluster members.

A number of clustering schemes have been proposed in recent years. These schemes inform the selection of CHs and the aggregation of data into clusters with network design. To reduce energy consumption of the sensor node during communication, various methods have been proposed. Clustering is defined as one of the main approaches to the development of protocols, which are effective, efficient, and scalable in WSNs. In clustering, the sensors of a network are grouped into small clusters, and each cluster is earmarked to a CH. After a certain interval of time, sensor nodes send the data to the CH, and it is the role of the CH to forward the data to the BS. Some benefits of clustering are listed below:

1. 1. Clustering sustains the bandwidth for communication.
2. 2. Clustering can balance the level of topology of the sensor network and reduce overhead traffic, with decreased overall maintenance costs.
3. 3. It controls repetition of the substituted information between the nodes.
4. 4. It helps to extend the life of the network.
5. 5. Energy utilisation is decreased by management of the sleep cycle.
6. 6. Fewer data packets are sent over the network.
7. 7. Clustering prevents resending, reduces data repetition in the covered domain, and collisions are avoided.

A popular routing-based protocol based on clustering is LEACH, which is subdivided into rounds; in each respective round, the CH role is circulated between nodes, to maintain balance. With the intension of optimising energy use in the network, nodes are selected in terms of CH circularity and randomness. Based on the principle of proximity, normal nodes, which are referred to as cluster members, are a part of the corresponding CH. The main drawback of LEACH is that the CH selection is based upon probability, rather than the surplus energy of the nodes. Therefore, sometimes the wrong CH is selected, which has a small amount of energy, and, as a result, the CH dies out early. Table 2.4 shows a comparison of different parameters of LEACH and its variants [46–49]. The LEACH variants exhibit improved functioning than the standard LEACH protocol.

TABLE 2.4
Comparison of LEACH and its Variants

2.5 Mobile Agent-Based Clustering

Earlier research proposed clustering as a prime technique for achieving energy efficiency in WSN. This is because clustering allows the sensor nodes to gather the data at a single node, the CH, which forwards the data to BS, rather than allowing every single sensor node to send the data to the BS. The clustering evolves from the LEACH protocol, which uses three phases: CH selection, the formation of the cluster, and data forwarding via the CH. Clustering, in itself, suffers drawbacks, like random distribution of CH, greater energy consumption by the CH in single-hop communication with the BS, or unequal load balancing in multi-hop communication with the BS. Another approach to aggregate information from the sensor nodes is with the help of a mobile agent (MA) [59].

MA can migrate within technology that can migrate within a sensor network, and perform the assigned task such as data aggregation, data fusion, etc. It replaces the traditional client–server approach because of its different benefits. Table 2.5 shows a comparison between the traditional–client server model and the mobile agent model.

TABLE 2.5
Comparison between Traditional Client–Server Model and Mobile Agent Model

The MA has great advantages, such as short time of execution. This is because each and every node contributes effort to data aggregation. When the MA dispatched by the server in the network, it visits and collects interesting data, and aggregates it. The aggregated data is forwarded to the server by the MA. A number of MA-based networks have been developed by various researchers.

2.6 MA Itineraries in WSNs

There are different uses of MA in the context of WSN, for different applications, such as data aggregation, reprogramming, healthcare, intrusion detection, etc. The performance of all these applications is entirely dependent on the route followed by an MA. The itineraries for MA can be designed by either choosing single itinerary or multiple itineraries.

2.6.2 Multi-Itinerary Planning

SIP performs well for a small-scale network but it fails in large-scale networks, because of the following drawbacks:

1. i) As there is only one MA, which has to take a tour of hundreds of nodes within the network for data collection, this network suffers from long delays.
2. ii) There is a possibility that MA can be lost during migration to several nodes.
3. iii) The size of the data packet is increased when it aggregates from node to node and consumes more energy.
4. iv) As MA accumulates more and more data, its reliability decreases.

MIP overcomes these drawbacks by sending multiple MAs. Thus, MIP can be defined as an itinerary in which more than one MA is dispatched by the server in the network. The determination of the best possible route for MA is inspired by the arrangement of a global network in a large-scale network in which the MIP approach takes place. For calculations of the required number of Mas, along with their paths, the near-optimal itinerary design (NOID) algorithm was introduced in [63]. The distance between source nodes is the main parameter used in cost–weight calculations. This distance is calculated based on the minimum spanning tree (MST). Thus, it acts as a compromise to balance, so that the MA first visits a source node with inadequate energy (where the MA data are less).

However, the BST–MIP algorithm is differentiated from the NOID due to the fact that, at some stage in weight calculation in a totally connected graph (TCG), which indicates that all source nodes in a particular sub-tree should be considered as a group, it makes use of a balance factor [64]. The balance factor is used to get a flexible control between energy costs and task duration. A genetic algorithm (GA), based on MIP, was designed to be used to calculate the desired number of MAs, along with their MIP itineraries [65].

The idea behind GA-MIP is to determine the required number of MAs and their allocated source nodes, with the help of a two-tier coding method, based on GA. This coding acts as a gene that involves a series array (sequence) and a set array (group). The series array accommodates segments corresponding to a number of source nodes, associated with a MA. Each number of source arrays corresponds to source nodes for each segment. Each iteration uses two basic GA operations (crossover and mutation) and a fitness function for the selection of better genes. Despite good delay and energy consumption performances, GA-MIP also has a high computational complexity because of the need to maintain global network information in each iteration. In this GA-MIP referred to above, the geographical information of nodes and the cost of the MA itinerary are the main parameters which are used to determine the itineraries for the optimal number of MAs.

The greatest information in the greater memory based MIP (GIGM-MIP) algorithm takes geographical information into account in order to define useful number of MAs, along with their itineraries and data size in each partition [66]. The k-means clustering in GIGM-MIP tends to split the network into different clusters (partitions). The GIGM-MIP algorithm considers the data size of the source nodes for each cluster after partitioning the network. This data size determines the number of MAs allocated to a particular partition, so that each cluster is allocated more than one MA. The node with the largest free payload data is then assigned to the MA which has the maximum data size in comparison with the other nodes. This process is reiterated until all the MAs have almost the same size of payload data.

With this solution, a balance is maintained between the transported data and the distributed MAs, and it leads to a decrease in energy consumption. To find out the desired number of MAs and their itineraries, an immune-inspired algorithm is proposed, which is also called the Clonal Selection Algorithm for MIP (CSA-MIP) [67]. It uses the same two-level method of coding as the GA-MIP method. The difference between CSA-MIP and the previous MIP is that it uses an evolutionary two-stage search process for identifying overall search capabilities, with a different assigned mutation operator at each stage. While using these search methods, the solutions obtained from it result in variability in the number of MAs. In addition, the imbalance is decreased when the desired number of sensor nodes is assigned to each MA. This situation may give rise to increased chances of achieving better solutions (Table 2.6).

TABLE 2.6
Comparison of Different Algorithms of MA Itineraries

 

2.7 Trust-aware WSN

The MA-based data aggregation provides efficient and effective data aggregation, based on the MA itinerary. In addition to the migration of these energy-efficient MAs, another important matter of interest is the safety of MA in terms of malicious or compromised nodes. The topic of trust is an open and current field in WSN. A number of trust schemes have been surveyed and studied from different perspectives. When MA encounters a malicious node, a false reprogramming can be done by it which, in turn, results in corruption of the network, stopping the functioning of the MA, etc.

In the past few years, trust management has become included in the literature for assessing the node’s trust value, based on the functional behaviour of the protocol. This assessed or calculated trust value can also be used for decision making with respect to data routing, data aggregation, and intrusion detection [68]. Two approaches to data aggregation in a trust-aware network are suggested in [69], where the first is the aggregation input collection and the second one is an inconsistency check. Data are aggregated at regular intervals of time, where the data are checked for inconsistency, if there is any need to calculate the trust of nodes.

Another secure algorithm for selecting the CH is achieved by calculating the weight of each node, with the intention of confirming that the minimum energy utilised is used for safe selection. The node’s weight combines different metrics, including trust metrics (node behaviour), which assists the decision to select the CH, which means that the node will never be a malicious one [70].

The trust metric is conclusive and permits the proposed algorithm to prevent the selection of a CH by any malicious node in the area, even if the remaining parameters support it. Other node metrics are also studied, which includes waiting time, degree of connectivity, and the distance between the nodes. The CH selection, using the weights of members’ nodes, is completed. MAEF is proposed, which is based on energy- and fault-conscious data aggregation in WSN. MA is sent to the selected CH, and this CH is chosen on the basis of the highest node density [71]. MA only visits selected CHs to collect data, which show improvements in network life, and also in energy consumption.

2.8 Summary and Conclusions

Today, the WSNs have immensely expanded their vital role in data-efficient selection and transport of data to destinations. The utilisation of energy resources is a critical issue for the networks, particularly in wireless networks, which are characterised by shorter battery lives. The complexity and dependence of corporate operations performed on WSNs require efficient routing techniques and protocols to ensure network interlinking and information routing, with the least energy expenditure. In this paper, the attention is given to the energy-efficient protocols, associated with WSNs.

With the requirements of the next generation of wireless networks and personal communication systems, a new type of system, based on the MA, has been developed. MA offers a unique and promising solution to increase the efficiency of energy use while aggregating data, with this agent-based approach being the most promising one for solving the data problem of WSN.

This approach can easily eliminate redundancy in data, with only the relevant specific data being transferred to the sink. MA will visit the network for data aggregation by two types of itineraries planned by the BS. It can follow either the single itinerary planning or multi-itinerary planning route. This paper also discussed trust metrics, issues related to the construction of a WSN, and issues dealing with trust management. There is no standard model available which can be used in current trust systems to offer safety or resilience to attacks. The designers are trying to solve the problem of trustworthiness from various angles. Future trust management research will focus on a generalised, scalable, and reconfigurable model of trust, which is accessible to different distributed computing systems, which deal with malicious and non-malicious nodes, while processing the data. Such a development will improve security problems in a better way and can meet the application demands of the consumer. Table 2.7 summarises the literature related to data aggregation, clustering, mobile agents, and trust-aware.

TABLE 2.7
Survey Related to Data Aggregation, Clustering, Mobile Agent and Trust-Aware

2.9 Future Scope

The relevant literature suggests some unique ideas, covering most of the areas of WSN, such as clustering, data aggregation, mobile agents, MA itineraries and trust-aware protocols. Many of the topics which are presented in this chapter are open for new discussions and for further research. This literature survey is intended for all who work with WSNs and related fields, to keep up with progress in the field. One of the most-important current topics in WSN is trust-aware, which needs to be explored in more detail. In trust-aware, a malicious node has to be detected by calculating the trust value before the data aggregation process is run. It is preferred over traditional cryptography methods because it has minimal computational cost.

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