With the growing concerns about energy saving and security problems in wireless sensor networks (WSNs), we investigated a joint energy saving scheduling and secure routing algorithm for critical event reporting in WSN. When a critical event has been detected, the notification must be safely sent to the sink node through the uplink transmission, and the sink node needs to broadcast the alarm messages to the whole network through the downlink transmission. In the uplink, a joint power allocation and secure routing strategy (JPASR) has been proposed to maximize the routing secure connection probability (RSCP) under the constraint of power. Meanwhile, combined with the level-by-level sleeping scheduling method, the energy-saving and secure uplink transmission can be guaranteed. In the downlink, an energy-first multi-point relays set selection mechanism (EFMSS) is designed to choose the backbone nodes to broadcast messages, and the backbone nodes are waken up by the same level-by-level sleeping scheduling method as the uplink transmission. With the two-step procedure, the critical events are appropriately dealt with and the responses are broadcast to the whole network. Extensive simulations results demonstrate that the energy saving and security performances of the proposed method are superior to the existing ones.

With recent adoption of Wireless Sensor-Actuator Networks (WSANs) in industrial automation, wireless control systems have emerged as a frontier of industrial networks. Hence, it has been shown that existing standards and researches concentrate on the reliability and real-time performance of WSANs. The multipath retransmission scheme with multiple channels is a key approach to guarantee the deterministic wireless communication. However, the efficiency of resource scheduling is seldom considered in applications with diverse data sampling rates. In this paper, we propose an efficient resources scheduling algorithm for multipath retransmission in WSANs. The objective of our algorithm is to improve efficiency and schedulability for the use of slot and channel resources. In detail, the proposed algorithm uses the approaches of CCA (clear channel assessment)-Embedded slot and Multiple sinks with Rate Monotonic scheme (CEM-RM) to decrease the number of collisions. We have simulated and implemented our algorithm in hardware and verified its performance in a real industrial environment. The achieved results show that the proposed algorithm significantly improves the schedulability without trading off reliability and real-time performance.

Wireless sensor networks (WSNs), as a collection of sensing nodes with limited processing, limited energy reserve and radio communication capabilities, are widely implemented in many areas of applications such as industry, environment, healthcare, etc. Regarding this large range of applications, many research issues are introduced including the applications, performance, reliability, lifetime, etc. The WSNs lifetime considered in this work is the period of time through which theWSN is perfectly completing its function. This lifetime is affected by many factors including the amount of energy available, failure probability and components degradation. The amount of energy available become the most important factor in case of non renewable components applications. Different algorithms, strategies and optimization techniques were developed and implemented for this purpose based on the possibility of activating a subset of sensors that satisfied the monitoring constraint, while keeping the others in sleep mode to be implemented later. This is an NP complete maximization problem that can be solved using disjoint set covers (DSCs). But the solution obtained using DSCs does not extend always significantly the WSNs lifetime. So, the present work aims to search for a better solution using non-disjoint set covers (NDSCs). This approach gives the opportunity for a sensor to be implemented in one or more subset covers. For that purpose, we studied a binary representation based model to maximize the number of NDSCs. Also, we developed a genetic algorithm based heuristic based on this model to find out the maximum number of NDSCs in a reasonable time. Thus, for a set of m sensors used to monitor a set of n targets or a field, this heuristic allows to construct a maximum number q of NDSCs. Additional effort is required to find the best scheduling for implementing the NDSCs so as to maximize the lifetime of the sensors involved in the WSNs, considering their limited available energy. This problem is formulated using integer linear programming (ILP) mathematical model. The objective function of this problem is the sum of all monitoring seasons on which all q NDSCs scheduled, and the constraint is the energy consumption in all sensors included in all NDSCs. Solving this problem using ILP in a period of time depends on the complexity of the model and the instances used. To find the solution in reasonable time, we have developed a NDSCs based genetic algorithm (NDSC-GA). The candidate solutions are represented in chromosomes composed of a number of genes equal to the number q of NDSCs, and each gene is the number of monitoring seasons on which a NDSC is scheduled. We have then developed a GA that combines the four crossover operators and four mutation operators. The GA based methods are coded in C programming language to obtain a satisfying solution and the Cplex software was used to obtain the corresponding exact solution. Comparing the optimal solution obtained using the ILP on small instances, to the solutions obtained using our GA based method explained that our methods can find a solution near the optimal in reasonable time. Then, comparing the solution obtained using our NDSCs GA based methods, to the DSCs GA based method in the literature, we showed that the NDSCs GA can prolong the WSNs lifetime better than DSCs GA for the same instances. Our approach combines together the scheduling principles and the optimization techniques to maximizing the WSNs lifetime

Wireless sensor networks (WSNs) can be used in agriculture to provide farmers with help monitoring the fields. Most of the people depend on agriculture. WSN plays a vital role in strengthening agriculture. In this chapter, the authors discuss energy-efficient routing with mobile sink protocols that are more suitable to strengthen the agriculture. They organize this chapter by classifying aforesaid protocols into three different categories (e.g., hierarchical-based, tree-based, and virtual structure-based routing). Strengthening Agriculture Through Energy-Efficient Routing in Wireless Sensor Networks Using Sink Mobility

Wireless sensor networks (WSNs) are predominantly used for monitoring applications. The sensor nodes are resource-constrained devices, and hence efficient energy utilization of these nodes is one of the major challenges. The communication distances directly impact on the energy consumption of the sensor nodes. Clustering methods are popularly used to reduce communication distances and prolong the network lifetime. Multi-sink deployment is another method to reduce communication distances. It also resolves congestion and hotspot issues. In multi-sink WSNs, the number of sinks to be considered is a challenging task as it affects the network topology, lifetime and deployment cost. In this research work, multi-sink deployment and clustering scheme with sink selection algorithm are jointly proposed to maximize the network lifetime and minimize the deployment cost. An iterative filtering model is proposed to estimate optimal number of sinks, while sink positions are determined based on Fuzzy logic inference system (FLIS). Fuzzy-c-means algorithm is used to form balanced clusters in the network. Cluster representative and sink selection processes are based on FLIS. The proposed optimal multi-sink deployment scheme reduces the deployment cost and the propagation delay of the system, while enhancing the network lifetime. The proposed scheme is also energy efficient in the case of higher node density. Hence, the proposed scheme can be suitably implemented for large-scale monitoring applications of WSNs.

Wireless Sensor Networks (WSNs) technology is one of the building blocks ofthe Internet of Things (IoT). Due to their features of easy deployment and flexibility,they are used in many application domains. Low-Power and Lossy Networks(LLNs) are a special type of WSNs in which nodes are largely resources constrained.For LLNs, convergecast is one of the basic traffic modes, where all traffic in the networkis destined to a predefined destination called the sink. While considering theIoT application domains, convergecast is not the only traffic mode in the network.The sink needs to send commands to certain sensors to perform actions. In this application,anycast is another basic traffic mode. In anycast, the traffic from the sinkis destined to any member of a group of potential receivers in the network.Traditionally LLNs are formed by static sensor nodes and rarely change positions.Due to the strict resource constraints in computation, energy and memory ofLLNs, most routing protocols only support static network. However, mobility hasbecome an important requirement for many emerging applications. In these applications,certain nodes are free to move and organize themselves into a connectednetwork. The topology would continuously change due to the movement of nodesand radio links instability. This is a hard task for most routing protocols of LLNs toadapt rapidly to the movement and to reconstruct topology in a timely manner.The goal of this thesis is to propose an efficient mobility support for routingprotocols in LLNs. We focus on convergecast and anycast, which are the most usedtraffic modes in LLNs, in mobile network scenarios.We propose an enhancement mechanism, named RL (RSSI and Level), to supportrouting protocols in convergecast LLNs in mobility. This mechanism helps routingprotocol make faster decisions for detecting mobility and updating next-hop neighborsbut suffers from high overhead. We propose a dynamic control message managementto enhance the overhead performance of RL and implement it on top ofRouting Protocol for Low-power and Lossy network (RPL) and we named it RRD(RSSI, Rank and Dynamic). After taking into account hysteresis of the coveragezone of the transmission range of nodes, we optimized RRD. This enhanced versionis called RRD+. Based on RRD+, we proposed MRRD+ (Multiple, RSSI, Rankand Dynamic) to support multiple sinks in convergecast LLNs in mobility. ADUP(Adaptive Downward/Upward Protocol) is a routing solution that supports bothconvergecast and anycast in LLNs concurrently.We evaluated the performance of our contributions in both simulation usingCooja simulator and experiment (only for ADUP) on TelosB motes. The resultsobtained in both simulation and experiment confirm the efficiency of our routingprotocols.

Wireless Sensor Networks (WSN) exhibit enormous potential in the realization of Cyber-Physical System (CPS) and Internet of Things (IoT) because of their suitability for a large number of applications in healthcare, agriculture, education, aviation, weather forecast, military, smart homes, manufacturing and many other domains. One of the established applications of WSN is localization of moving objects, which is integral part of monitoring, surveillance, intrusion detection and target tracking. For localizing a moving object in WSNs, generally, a set of location-aware static WSN nodes are used to localize the mobile nodes or moving target using a specific localization algorithm. RSSI and Trilateration based location identification is a well-known traditional method which needs distance calculation prior to localization. Many researchers have modified the traditional trilateration method to better suit their application or in general. In this paper we present, a modified trilateration method, which uses our application specific cooperative technique with better choice of beacon node placement to improve distance calculation method. The distance values are then used to expedite the trilateration process. The proposed technique has been simulated and compared with the traditional method. The results show that our proposed technique consumes less energy and ensures faster and complete localization through the deployed sensor nodes.

Wireless Sensor Networks (WSN) exhibit enormous potential in the realization of Cyber-Physical System (CPS) and Internet of Things (IoT) because of their suitability for a large number of applications in healthcare, agriculture, education, aviation, weather forecast, military, smart homes, manufacturing and many other domains. One of the established applications of WSN is localization of moving objects, which is integral part of monitoring, surveillance, intrusion detection and target tracking. For localizing a moving object in WSNs, generally, a set of location-aware static WSN nodes are used to localize the mobile nodes or moving target using a specific localization algorithm. RSSI and Trilateration based location identification is a well-known traditional method which needs distance calculation prior to localization. Many researchers have modified the traditional trilateration method to better suit their application or in general. In this paper we present, a modified trilateration method, which uses our application specific cooperative technique with better choice of beacon node placement to improve distance calculation method. The distance values are then used to expedite the trilateration process. The proposed technique has been simulated and compared with the traditional method. The results show that our proposed technique consumes less energy and ensures faster and complete localization through the deployed sensor nodes.

Wireless Sensor Network applications supporting diverse fields have gained a vital focus and interest from the researchers these days. WSN applications are designed to support scalable real time environment monitoring that depends on region of implication. The major challenges in WSN include energy optimization, routing, obstacle identification, security, etc. These constraints need prioritized optimization in fulfilling the requirements of the deployed environment. Many research approaches provide solutions for each of the influencing factors in WSN. We intend to survey the energy optimization techniques in WSN with distinct considerations. This paper focuses in surveying preceding optimization techniques under multi-objective perspective that results in tradeoffs. We exclude the familiar optimization approaches and analyze distinct metric specific optimizations based on link failure, load balancing and distance.

Wireless Sensor Network (WSN) is consisting of nodes which deployed to sense the physical environment. In WSN there is excessive growth in performance evaluation and analysis techniques, because of fast development in Information and Communication Technologies (ICT). The main resource problem in WSN is the energy of sensor nodes. There is need of routing protocol which consumes less energy during communication. A reliable and robust routing protocol which limits the excess energy consumed by nodes during forwarding and receiving messages results in energy efficiency protocol. This survey work provides the advantages, drawbacks and comparison of five energy efficient routing protocols.

Wireless Sensor Network (WSN) is a network which formed with a maximum number of sensor nodes which are positioned in an application environment to monitor the physical entities in a target area, for example, temperature monitoring environment, water level, monitoring pressure, and health care, and various military applications. Mostly sensor nodes are equipped with self-supported battery power through which they can perform adequate operations and communication among neighboring nodes. Maximizing the lifetime of the Wireless Sensor networks, energy conservation measures are essential for improving the performance of WSNs. This paper proposes an Enhanced PSO-Based Clustering Energy Optimization (EPSO-CEO) algorithm for Wireless Sensor Network in which clustering and clustering head selection are done by using Particle Swarm Optimization (PSO) algorithm with respect to minimizing the power consumption in WSN. The performance metrics are evaluated and results are compared with competitive clustering algorithm to validate the reduction in energy consumption.

We study a heterogeneous wireless sensor network (WSN) where N heterogeneous access points (APs) gather data from densely deployed sensors and transmit their sensed information to M heterogeneous fusion centers (FCs) via multi-hop wireless communication. This heterogeneous node deployment problem is modeled as an optimization problem with total wireless communication power consumption of the network as its objective function. We consider both static WSNs, where nodes retain their deployed position, and mobile WSNs where nodes can move from their initial deployment to their optimal locations. Based on the derived necessary conditions for the optimal node deployment in static WSNs, we propose an iterative algorithm to deploy nodes. In addition, we study the necessary conditions of the optimal movement-efficient node deployment in mobile WSNs with constrained movement energy, and present iterative algorithms to find such deployments, accordingly. Simulation results show that our proposed node deployment algorithms outperform the existing methods in the literature, and achieves a lower total wireless communication power in both static and mobile WSNs, on average. Index Terms Node deployment, heterogeneous multi-hop wireless sensor networks, power optimization, network lifetime.

Using multiple sink nodes in wireless sensor networks can greatly improve the lifetime and throughput of the network. One of the important issues in multi-sink wireless sensor networks is the congestion problem in sink nodes which reduces the effectiveness of data processing in sink nodes and increases the energy consumption of the sensor nodes. To prevent congestion in the sink nodes, in this paper, a distributed fuzzy logic-based sink selection algorithm is presented for one-hop sensor networks which makes each sensor node able to independently select a sink node based on the congestion situations in all the sink nodes. This scheme can effectively prevent congestion in the sink nodes and provide load balancing among them. Moreover, it reduces the delay of successful data transmissions to the sink nodes and improves the lifetime of wireless sensor networks by reducing the number of packet retransmissions. Simulation results indicate the effectiveness of our proposed solution.

This paper presents a novel hybrid simulation method based on the combination of an in-house developed 3D ray launching algorithm and a collaborative filtering (CF) technique, which will be used to analyze the performance of ZigBee-based wireless sensor networks (WSNs) to enable ambient assisted living (AAL). The combination of Low Definition results obtained by means of a deterministic ray launching method and the application of a CF technique leads to a drastic reduction of the time and computational cost required to obtain accurate simulation results. The paper also reports that this kind of AAL indoor complex scenario with multiple wireless devices needs a thorough and personalized radioplanning analysis as radiopropagation has a strong dependence on the network topology and the specific morphology of the scenario. The wireless channel analysis performed by our hybrid method provides valuable insight into network design phases of complex wireless systems, typical in AAL-oriented environments. Thus, it results in optimizing network deployment, reducing overall interference levels, and increasing the overall system performance in terms of cost reduction, transmission rates, and energy efficiency.

There are some underwater areas with high ecological interest that should be monitored. Posidonia and seagrasses exert considerable work in protecting the coastline from erosion. In these areas, many animals and organisms live and find the grassland food and the protection against predators. It is considered a bioindicator of the quality of coastal marine waters. It is important to monitor them and maintain these ecological communities as clean as possible. In this paper, we present an oceanographic buoy for Posidonia meadows monitoring. It is based on a set of low cost sensors which are able to collect data from water such as salinity, temperature, and turbidity and from the weather as temperature, relative humidity, and rainfall, among others. The system is mounted in a buoy which keeps it isolated to possible oxidation problems. Data gathered are processed using a microcontroller. Finally the buoy is connected with a base station placed on the mainland through a wireless connection using a FlyPort module. The network performance is checked in order to ensure that no delays will be generated on the data transmission. This proposal could be used to monitor other areas with special ecological interest and for monitoring and supervising aquaculture activities.

The sensor nodes communicate together by wireless techniques, and these communication techniques are handled by routing protocols. The resource limitation and unreliable low power links between the sensor nodes make it difficult to design an efficient routing protocol. The sink may be either static or mobile in the network. In many scenarios, static sink causes hotspots, where the sensor nodes near to the sink die out soon due to transmission overhead. On the other hand, the mobile sink improves the lifetime of a network by avoiding excessive transmission overhead on the nodes that are close to the sink. Further, an attempt is made to resolve the issues of sensor nodes and sink mobility by proposing energy-efficient routing techniques for wireless sensor network. A multipath routing protocol (MRP) is proposed, which reduces the control overhead for route discovery and increases the throughput of the network. The proposed multipath routing protocol is designed to improve the lifetime, latency and reliability through discovering multiple paths from the source node to the sink. MRP is a sink initiated route discovery process, where source node location is known. In MRP, one primary path and number of alternate paths are discovered. The sink may receive redundant data due to densely deployed sensor nodes. Clustering the sensor nodes is an effective way to reduce the redundancy. The cluster head aggregates the cluster members’ data before transmitting it to the sink. A cluster based multipath routing protocol (CMRP) is proposed, where the clustering technique reduces the data traffic in the network, and multipath technique provides the reliable path. Although, the hotspot problem can be resolved with mobile sink, it makes the network dynamic. A tree-based data dissemination protocol with mobile sink called TEDD is proposed to overcome the above problems. TEDD manages the mobility of the sink and balances the load among the sensor nodes to maximize the lifetime. A sensor node initiates the tree construction and becomes the root node of the tree. Sensor nodes can send the data to the sink using this tree. It has been observed that the TEDD is a robust and energy-efficient protocol in themobile sink environment. The proposed dense tree based routing protocol (DTRP) is an extension of TEDD. The objectives of DTRP are to minimize the control overhead and reduce the path length. Both the objectives are achieved by reducing the number of relay nodes in the tree structure. DTRP resulted in, increased lifetime and reduced end-to-end latency. A clustered tree based routing protocol (CTRP) is designed to reduce thedata traffic in the network and efficiently manage the sink mobility. The traffic is reduced by the cluster head, which uses the aggregation technique. The number of cluster heads is restricted to the number of grids present in the network. The CTRP efficiently manages the load among the sensor nodes. The tree is constructed in the network using the cluster heads as vertices. The data can be transmitted to the sink through the tree structure. The CTRP is compared with the TEDD and DTRP in terms of energy efficiency, end-to-end latency, data delivery ratio and network lifetime. For the time-sensitive applications, a rendezvous based routing (RRP) with mobile sink is designed. Each sensor node can communicate with the rendezvous region. In RRP, two methods for data transmission are proposed. In the first method, source node directly transmits their sensory data to the rendezvous area. In the second method, the source node retrieves the sink’s current position and sends the data to the sink through intermediate nodes. The end-to-end latency and data delivery ratio are improved in the first proposed method. Whereas, the energy consumption and lifetime in the second proposed method are enhanced.

The ideal utilization of radio spectra is a major issue of concern in the field of wireless communication. Increasing demand for wireless radio services has led to the issue of frequency scarcity. Therefore, in order to accommodate more and more users, cognitive radio technology came into existence. The adaptive nature of cognitive radio helps them enhance the spectral efficiency, thereby utilizing the available spectra without causing any interference for the licensed users. The primary task of cognitive radio lies in the spectrum sensing and identification of holes. But the presence of a single CR and multiple secondary users in the network can lead to delay and collision. Therefore, the algorithm named “multiple CRs single-hop (MCSH) secondary user cognitive radio network architecture” has been formulated and proposed in which multiple CRs can coordinate with each other via single hop as well as with unlicensed users in order to diminish the delay, jitter, and packet loss.

Summary Mobile agent (MA)-based middleware has been thoroughly investigated in the past few years as a means to address the efficiency, scalability, and reliability issues of data fusion applications on wireless sensor networks. Deriving an efficient itinerary for each MA to follow is of high importance, because itineraries determine to a large extent the overall performance of data fusion tasks. In this article, we present a novel algorithmic approach for efficient itinerary planning of MA objects undertaking data fusion tasks. We adopt a method based on iterated local search to construct the itineraries (ie, visiting sequences of source nodes) assigned to multiple traveling MAs. We apply alternative optimization criteria which aim either at minimizing the overall energy expenditure over all derived MA itineraries or prolonging the network lifetime. Furthermore, we propose algorithmic solutions for 2 realistic settings which have not been investigated in the past: firstly, the employment of multiple sinks that share the responsibility of MA-based data fusion tasks across the sensor field, and secondly, the consideration of heterogeneous sensor networks comprising nodes powerful enough to host the runtime environment required to execute MA code as well as “ordinary” nodes which lack these resources. Simulation tests verify the performance gain attained by our algorithmic methods against alternative itinerary planning approaches which involve multiple MAs. Copyright © 2016 John Wiley & Sons, Ltd.

Recently, the development of wireless sensor networks (WSNs) is spreading rapidly. WSNs are highly distributed self-organized systems which comprise a large number of resource constrained sensor nodes. Developers of WSNs face many challenges from communication, memory, limited energy... Also, mobility has become a major concern for WSN researchers. Indeed, Mobile WSNs (MWSN) consist of mobile sensor nodes that can move on their own and also interact with the physical environment. Developing applications for MWSN is a complicated process because of the wide variety of WSN applications and low-level implementation details. Integrating contextawareness can improve MWSN applications results. In this paper, some research issues and challenges involved in the design of WSNs are presented. Model-Driven Engineering offers an effective solution to WSN application developers by hiding the details of lower layers and raising the level of abstraction. In this sense, we propose a context-aware WSN architecture and WSN metamodel to ease the work for developers in this field.

Quality of sensing is a fundamental research topic in sensor networks. In this paper, we propose an adaptive sensing technique to guarantee the end-to-end reliability while maximizing the lifetime of sensor networks under additive white Gaussian noise channels. First, we conduct theoretical analysis to obtain optimal node number , node placement , and node transmission structure under minimum total energy consumption and minimum unit data transmission energy consumption. Then, because sensor nodes closer to the sink consume more energy, nodes far from the sink have more residual energy. Based on this observation, we propose an adaptive sensing technique to achieve balanced network energy consumption. It adopts lower reliability requirement and shorter transmission distance for nodes near the sink and adopts higher reliability requirement and farther transmission distance for nodes far from the sink. Theoretical analysis and experimental results show that our design can improve the network lifetime by several times (1–5 times) and network utility by 20% and the desired reliability level is also guaranteed.