IRIS: Integrated data gathering and interest dissemination system for wireless sensor networks

This paper presents IRIS, an integrated interest dissemination and convergecasting solution for wireless sensor networks (WSNs). The interest dissemination protocol is used to build and maintain the network topology and for task/instruction assignment, while convergecasting implements data gathering at the network sink. Convergecasting heavily exploits cross-layering in that MAC and routing operation are performed jointly and relay selection is based on flexible cost functions that take into account information from different layers. The definition of the IRIS cost function enables tradeoff between key end-to-end performance metrics. In addition, it provides mechanisms for supporting efficient network behavior such as in-network data aggregation or processing. Energy usage is minimized by exploiting density estimation, sleeping modes and duty cycle control in a distributed and autonomous manner and as a function of the traffic intensity. Finally, IRIS is self adaptive, highly localized and imposes limited control overhead. IRIS performance is evaluated through ns2 simulations as well as through experiments on a WSN testbed. Comparative performance results show that IRIS outperforms previous cross-layer solutions. The flexibility introduced by the IRIS cross-layer approach results in higher robustness than that of well-known approaches such as BoX-MAC and CTP.     

Energy-efficient target detection in sensor networks using line proxies

One of the fundamental and important operations in sensor networks is sink–source matching, i.e. target detection. Target detection is about how a sink finds the location of source nodes observing the event of interest (i.e. target activity). This operation is very important in many sensor network applications such as military battlefield and environment habitats. The mobility of both targets and sinks brings significant challenge to target detection in sensor networks. Most existing approaches are either energy inefficient or lack of fault tolerance in the environment of mobile targets and mobile sinks. Motivated by these, we propose an energy-efficient line proxy target detection (LPTD) approach in this paper. The basic idea of LPTD is to use designated line proxies as rendezvous points (or agents) to coordinate mobile sinks and mobile targets. Instead of having rendezvous nodes for each target type as used by most existing approaches, we adopt the temporal-based hash function to determine the line in the given time. Then the lines are alternated over time in the entire sensor network. This simple temporal-based line rotation idea allows all sensor nodes in the network to serve as rendezvous points and achieves overall load balancing. Furthermore, instead of network-wide flooding, interests from sinks will be flooded only to designated line proxies within limited area. The interest flooding can further decrease if the interest has geographical constraints. We have conducted extensive analysis and simulations to evaluate the performance of our proposed approach. Our results show that the proposed approach can significantly reduce overall energy consumption and target detection delay. Copyright © 2007 John Wiley & Sons, Ltd.     

The Impact of Deployment Pattern and Routing Scheme on the Lifetime in Multi-Sink Wireless Sensor Network

Extensive use of sensor and actuator networks in many real-life applications introduced several new performance metrics at the node and network level. Since wireless sensor nodes have significant battery constraints, therefore, energy efficiency, as well as network lifetime, are among the most significant performance metrics to measure the effectiveness of given network architecture. This work investigates the performance of an event-based data delivery model using a multipath routing scheme for a wireless sensor network with multiple sink nodes. This routing algorithm follows a sink initiated route discovery process with the location information of the source nodes already known to the sink nodes. It also considers communication link costs before making decisions for packet forwarding. Carried out simulation compares the network performance of a wireless sensor network with a single sink, dual sink, and multi sink networking approaches. Based on a series of simulation experiments, the lifetime aware multipath routing approach is found appropriate for increasing the lifetime of sensor nodes significantly when compared to other similar routing schemes. However, energy-efficient packet forwarding is a major concern of this work; other network performance metrics like delay, average packet latency, and packet delivery ratio are also taken into the account.

     

Efficient Sensing Topology Management for Spatial Monitoring with Sensor Networks

We focus on exploiting redundancy for sensor networks in the context of spatial interpolation. The network acts as a distributed sampling system, where sensors periodically sample a physical phenomenon of interest, e.g. temperature. Samples are then used to construct a continuous spatial estimate of the phenomenon over time through interpolation. In this regime, the notion of sensing range typically utilized to characterize redundancy in event detection applications is meaningless and sensor selection schemes based on it become unsuitable. Instead, this paper presents pragmatic approaches for exploiting redundancy in such applications. Their underlying characteristic is that no a-priori assumptions need to be made on the statistical properties of the physical phenomenon. These are instead learned by the network after deployment. Our approaches are evaluated through real as well as synthetic sensor network data showing that significant reductions in the number of active sensors are indeed possible.     

Mobile agent migration modeling and design for target tracking in wireless sensor networks

Computing paradigms play an important and fundamental role in collaborative processing in wireless sensor networks. The client/server based paradigm and the mobile agent based paradigm are two popular computing models used to facilitate collaboration among sensor nodes. In this paper, we study the key problem of determining the mobile agent itinerary for collaborative processing and model the dynamic mobile agent planning problem. We then present three itinerary planning algorithms, the static, the dynamic, and the predictive dynamic approaches to solve the target tracking problem in wireless sensor networks. We design three metrics (energy consumption, network lifetime, and the number of hops) and use simulation tools to quantitatively measure the performance of different itinerary planning algorithms. Simulation results show considerable improvement over the static itinerary and the dynamic itinerary approaches using the predictive dynamic itinerary algorithm.     

RAD‐EI: A routing attack detection scheme for edge‐based Internet of Things environment

Internet of Things (IoT) offers various types of application services in different domains, such as “smart infrastructure, health‐care, critical infrastructure, and intelligent transportation system.” The name edge computing signifies a corner or edge in a network at which traffic enters or exits from the network. In edge computing, the data analysis task happens very close to the IoT smart sensors and devices. Edge computing can also speed up the analysis process, which allows decision makers to take action within a short duration of time. However, edge‐based IoT environment has several security and privacy issues similar to those for the cloud‐based IoT environment. Various types of attacks, such as “replay, man‐in‐the middle, impersonation, password guessing, routing attack, and other denial of service attacks” may be possible in edge‐based IoT environment. The routing attacker nodes have the capability to deviate and disrupt the normal flow of traffic. These malicious nodes do not send packets (messages) to the edge node and only send packets to its neighbor collaborator attacker nodes. Therefore, in the presence of such kind of routing attack, edge node does not get the information or sometimes it gets the partial information. This further affects the overall performance of communication of edge‐based IoT environment. In the presence of such an attack, the “throughput of the network” decreases, “end‐to‐end delay” increases, “packet delivery ratio” decreases, and other parameters also get affected. Consequently, it is important to provide solution for such kind of attack. In this paper, we design an intrusion detection scheme for the detection of routing attack in edge‐based IoT environment called as RAD‐EI. We simulate RAD‐EI using the widely used “NS2 simulator” to measure different network parameters. Furthermore, we provide the security analysis of RAD‐EI to prove its resilience against routing attacks. RAD‐EI accomplishes around 95.0% “detection rate” and 1.23% “false positive rate” that are notably better than other related existing schemes. In addition, RAD‐EI is efficient in terms of computation and communication costs. As a result, RAD‐EI is a good match for some critical and sensitive applications, such as smart security and surveillance system.     

Placing and maintaining a core node in wirelessad hoc networks

Wireless ad hoc networks are characterized by several performance metrics, such as bandwidth, transport, delay, power, etc. These networks are examined by constructing a tree network. A core node is usually chosen to be the median or center of the multicast tree network with a tendency to minimize a performance metric, such as delay or transport. In this paper, we present a new efficient strategy for constructing and maintaining a core node in a multicast tree for wireless ad hoc networks undergoing dynamic changes, based on local information. The new core (centdian) function is defined by a convex combination signifying total transport and delay metrics. We provide two bounds of O(d) and O(d+l) time for maintaining the centdian using local updates, where l is the hop count between the new center and the new centdian, and d is the diameter of the tree network. We also show an O(n log n) time solution for finding the centdian in the Euclidian complete network. Finally, an extensive simulation for the construction algorithm and the maintenance algorithm is presented along with an interesting observation. Copyright © 2009 John Wiley & Sons, Ltd.     

Routing characteristics of ad hoc networks with unidirectional links

Unidirectional links in an ad hoc network can result from factors such as heterogeneity of receiver and transmitter hardware, power control or topology control algorithms, or differing sources of interference or jamming. Previously proposed metrics for evaluating the difficulty of a unidirectional scenario are limited in scope and are frequently misleading. To be able to analyze ad hoc network routing protocol behavior in a complex networking environment, it is not sufficient to merely assign a single level of difficulty to a unidirectional network scenario; the many interrelated routing characteristics of these scenarios must be understood. In this paper, we develop a set of metrics for describing these characteristics, for example for characterizing routing scenarios in simulations, analysis, and testbed implementations. Based on these metrics, we perform a detailed simulation analysis of the routing characteristics of the three most common simulation models for generating unidirectional links in ad hoc networks: the random-power model, the two-power model, and the three-power model. Our findings enable protocol designers to better choose a set of network scenarios and parameters that truly explore a wide range of a routing protocol’s behaviors in the presence of unidirectional links, and to better understand the complex interplay between routing mechanisms and network conditions.     

Blind Adaptive Weighted Aggregation Scheme for Event Detection in Multihop Wireless Sensor Networks

The problem of decision fusion for event detection in Wireless Sensor Networks is the prime focus of this paper. Our proposed algorithm focuses on single hop (star) and multihop (tree) topologies, which are commonly deployed wireless sensor network topologies. In order to minimize the overall energy consumption in the network, a transmission constraint of one-bit is imposed on each sensor node. This poses a challenging problem of designing a one-bit decision fusion rule at every fusion center, which improves the overall detection accuracy at the sink node. The absence of apriori knowledge of each sensor’s local performance indices, makes the existing optimum fusion rule infeasible. Moreover, in the absence of a training sequence of true event occurrences, existing Adaptive distributed detection techniques also become inapplicable. In this setup, the key contribution of this paper is a Least Mean Squares based Blind Adaptive Weighted Aggregation Scheme (Blind-AdWAS) for Wireless Sensor Networks with tree topology. We extend our earlier work (Jagyasi et al. in Proceedings of 11th international symposium on wireless personal multimedia communication, 2008) to include an analysis of the effect of Rayleigh flat fading channel on Blind-AdWAS in comparison with existing channel-aware optimum and sub- optimum aggregation schemes. Even in the absence of any channel knowledge or knowledge of performance indices, Blind-AdWAS demonstrates robustness in event detection performance.     

Flow-balanced routing for multi-hop clustered wireless sensor networks

Power efficiency and coverage preservation are two important performance metrics for a wireless sensor network. However, there is scarcely any protocol to consider them at the same time. In this paper, we propose a flow-balanced routing (FBR) protocol for multi-hop clustered wireless sensor networks that attempts to achieve both power efficiency and coverage preservation. The proposed protocol consists of four algorithms, one each for network clustering, multi-hop backbone construction, flow-balanced transmission, and rerouting. The proposed clustering algorithm groups several sensors into one cluster on the basis of overlapping degrees of sensors. The backbone construction algorithm constructs a novel multi-level backbone, which is not necessarily a tree, using the cluster heads and the sink. Furthermore, the flow-balanced routing algorithm assigns the transferred data over multiple paths from the sensors to the sink in order to equalize the power consumption of sensors. Lastly, the rerouting algorithm reconstructs the network topology only in a place where a head drops out from the backbone due to the head running out of its energy. Two metrics called the network lifetime and the coverage lifetime are used to evaluate the performance of FBR protocol in comparison with previous ones. The simulation results show that FBR yields both much longer lifetime and better coverage preservation than previous protocols. For example, FBR yields more than twice network lifetime and better coverage preservation than a previous efficient protocol, called the coverage-preserving clustering protocol (CPCP) [18], when the first sensor dies and the network coverage is kept at 100%, respectively.     

A distributed reinforcement learning based sensor node scheduling algorithm for coverage and connectivity maintenance in wireless sensor network

The fundamental challenge for randomly deployed resource-constrained wireless sensor network is to enhance the network lifetime without compromising its performance metrics such as coverage rate and network connectivity. One way is to schedule the activities of sensor nodes and form scheduling rounds autonomously in such a way that each spatial point is covered by at least one sensor node and there must be at least one communication path from the sensor nodes to base station. This autonomous activity scheduling of the sensor nodes can be efficiently done with Reinforcement Learning (RL), a technique of machine learning because it does not require prior environment modeling. In this paper, a Nash Q-Learning based node scheduling algorithm for coverage and connectivity maintenance (CCM-RL) is proposed where each node autonomously learns its optimal action (active/hibernate/sleep/customize the sensing range) to maximize the coverage rate and maintain network connectivity. The learning algorithm resides inside each sensor node. The main objective of this algorithm is to enable the sensor nodes to learn their optimal action so that the total number of activated nodes in each scheduling round becomes minimum and preserves the criteria of coverage rate and network connectivity. The comparison of CCM-RL protocol with other protocols proves its accuracy and reliability. The simulative comparison shows that CCM-RL performs better in terms of an average number of active sensor nodes in one scheduling round, coverage rate, and energy consumption.

     

A communication‐efficient framework for outlier‐free data reporting in data‐gathering sensor networks

In this paper, we address the problem of reducing the communication cost and hence the energy costs incurred in data‐gathering applications of a sensor network. Environmental data depicts a huge amount of correlation in both the spatial and temporal domains. We exploit these temporal–spatial correlations to address the aforementioned problem. More specifically, we propose a framework that partitions the physical sensor network topology into a number of feature regions. Each sensor node builds a data model that represents the underlying structure of the data. A representative node in each feature region communicates only the model coefficients to the sink, which then uses them to answer queries. The temporal and spatial similarity has special meaning in outlier cleaning too. We use a modified z‐score technique to precisely label the outliers and use the spatial similarity to confirm whether the outliers are due to a true change in the phenomenon under study or due to faulty sensor nodes. Copyright © 2008 John Wiley & Sons, Ltd.     

Sink-to-sensors congestion control

The problem of congestion in sensor networks is significantly different from that of conventional ad-hoc networks and has not been studied to any great extent thus far. In this paper, we focus on providing congestion control from the sink to the sensors in a sensor field. We identify the different reasons for congestion from the sink to the sensors and show the uniqueness of the problem in sensor network environments. We propose a generic framework that addresses congestion from the sink to the sensors in a sensor network. Through ns2 based simulations, we evaluate the proposed approach and compare its performance with three baseline approaches.     

Optimal placement and routing strategies for resilient two‐tiered sensor networks

In hierarchical sensor networks using relay nodes, sensor nodes are arranged in clusters and higher powered relay nodes can be used as cluster heads. The lifetime of such a network is determined primarily by the lifetime of the relay nodes. In this paper, we propose two new integer linear programs (ILPs) formulations for optimal data gathering, which maximize the lifetime of the upper tier relay node network. Unlike most previous approaches considered in the literature, our formulations can generate optimal solutions under the non‐flow‐splitting model. Experimental results demonstrate that our approach can significantly extend network lifetime, compared to traditional routing schemes, for the non‐flow‐splitting model. The lifetime can be further enhanced by periodic updates of the routing strategy based on the residual energy at each relay node. The proposed rescheduling scheme can be used to handle single or multiple relay node failures. We have also presented a very simple and straightforward algorithm for the placement of relay nodes. The placement algorithm guarantees that all the sensor nodes can communicate with at least one relay node and that the relay node network is at least 2‐connected. This means that failure of a single relay node will not disconnect the network, and data may be routed around the failed node. The worst case performance of the placement algorithm is bounded by a constant with respect to any optimum placement algorithm. Copyright © 2008 John Wiley & Sons, Ltd.     

Aggregate node placement for maximizing network lifetime in sensor networks

Sensor networks have been receiving significant attention due to their potential applications in environmental monitoring and surveillance domains. In this paper, we consider the design issue of sensor networks by placing a few powerful aggregate nodes into a dense sensor network such that the network lifetime is significantly prolonged when performing data gathering. Specifically, given K aggregate nodes and a dense sensor network consisting of n sensors with K ≪ n, the problem is to place the K aggregate nodes into the network such that the lifetime of the resulting network is maximized, subject to the distortion constraints that both the maximum transmission range of an aggregate node and the maximum transmission delay between an aggregate node and its covered sensor are met. This problem is a joint optimization problem of aggregate node placement and the communication structure, which is NP‐hard. In this paper, we first give a non‐linear programming solution for it. We then devise a novel heuristic algorithm. We finally conduct experiments by simulation to evaluate the performance of the proposed algorithm in terms of network lifetime. The experimental results show that the proposed algorithm outperforms a commonly used uniform placement schema — equal distance placement schema significantly. Copyright © 2010 John Wiley & Sons, Ltd.     

On improving the representation of a region achieved by a sensor network

This paper considers the class of applications of sensor networks in which each sensor node makes measurements, such as temperature or humidity, at the precise location of the node. Such spot-sensing applications approximate the physical condition of the entire region of interest by the measurements made at only the points where the sensor nodes are located. Given a certain density of nodes in a region, a more spatially uniform distribution of the nodes leads to a better approximation of the physical condition of the region in the sensed data. This paper considers the error in this approximation and seeks to improve the quality of representation of the physical condition of the points in the region in the data collected by the sensor network. We develop two essential metrics which together allow a rigorous quantitative assessment of the quality of representation achieved: the average representation error and the unevenness of representation error, the latter based on a well-accepted measure of inequality used in economics. We present the rationale behind the use of these metrics and derive relevant theoretical bounds on them in the common scenario of a planar region of arbitrary shape covered by a sensor network deployment. A simple new heuristic algorithm is presented for each node to determine if and when it should sense or sleep to conserve energy while also preserving the quality of representation. Simulation results show that it achieves a significant improvement in the quality of representation compared to other related distributed algorithms. Interestingly, our results also show that improved and consistent spatial uniformity has the welcome side-effect of a significant increase in the network lifetime.     

Spanning tree based algorithms for low latency and energy efficient data aggregation enhanced convergecast (DAC) in wireless sensor networks

Many wireless sensor networks (WSNs) employ battery-powered sensor nodes. Communication in such networks is very taxing on its scarce energy resources. Convergecast – process of routing data from many sources to a sink – is commonly performed operation in WSNs. Data aggregation is a frequently used energy-conversing technique in WSNs. The rationale is to reduce volume of communicated data by using in-network processing capability at sensor nodes. In this paper, we address the problem of performing the operation of data aggregation enhanced convergecast (DAC) in an energy and latency efficient manner. We assume that all the nodes in the network have a data item and there is an a priori known application dependent data compression factor (or compression factor), γ, that approximates the useful fraction of the total data collected.The paper first presents two DAC tree construction algorithms. One is a variant of the Minimum Spanning Tree (MST) algorithm and the other is a variant of the Single Source Shortest Path Spanning Tree (SPT) algorithm. These two algorithms serve as a motivation for our Combined algorithm (COM) which generalized the SPT and MST based algorithm. The COM algorithm tries to construct an energy optimal DAC tree for any fixed value of α (= 1 − γ), the data growth factor. The nodes of these trees are scheduled for collision-free communication using a channel allocation algorithm. To achieve low latency, these algorithms use the β-constraint, which puts a soft limit on the maximum number of children a node can have in a DAC tree. The DAC tree obtained from energy minimizing phase of tree construction algorithms is re-structured using the β-constraint (in the latency minimizing phase) to reduce latency (at the expense of increasing energy cost). The effectiveness of these algorithms is evaluated by using energy efficiency, latency and network lifetime as metrics. With these metrics, the algorithms’ performance is compared with an existing data aggregation technique. From the experimental results, for a given network density and data compression factor γ at intermediate nodes, one can choose an appropriate algorithm depending upon whether the primary goal is to minimize the latency or the energy consumption.     

Performance comparison of end‐to‐end and on‐the‐spot traffic‐aware techniques

Traffic‐aware routing protocols have recently received considerable attention. This has been motivated by the role that it could play in extending the network's lifetime (or operational time) as it target the reservation of node's power and better utilisation of channel bandwidth, which could lead to performance improvement in terms of important performance metrics including throughput and end‐to‐end delay. Traffic‐aware techniques can be classified into two categories, namely end‐to‐end and on‐the‐spot, based on the way of establishing and maintaining routes between any source and destination. Although there has been much work on both categories, there has been no comparative performance study of the two approaches. To the best of our knowledge, this is the first work that carries out such a performance comparison. To this end, we have adapted our traffic‐aware technique namely load density to suggest a new ‘on‐the‐spot’ traffic‐aware technique. The main reason for doing this adaptation is to ensure that the comparison between the two approaches is fair and realistic. The study will reveal the main performance characteristics of the two approaches under various traffic and network conditions. Copyright © 2011 John Wiley & Sons, Ltd.     

Optimizing event detection in low duty-cycled sensor networks

Duty cycling is a fundamental approach to conserving energy in sensor networks; however, it brings challenges to event detection in the sense that an event may be undetected or undergo a certain delay before it is detected, in particular when sensors are low duty-cycled. We investigate the fundamental relationship between event detection and energy efficiency. We quantify event detection performance by deriving the closed forms of detection delay and detectability with a relatively simple model. We also characterize the intrinsic tradeoff that exists between detection performance and system lifetime, which helps flexible design decisions for sensor networks. In addition, we propose a fully localized algorithm called CAS to cooperatively determine sensor wakeups. Without relying on location information, the distributed algorithm is easy to implement and scalable to network density and scale. Theoretical bounds of event detection are also studied to facilitate comparative study. Comprehensive experiments are conducted and results demonstrate that the proposed algorithm significantly improves event detection performance in terms of detection latency and detection probability. It reduces as high as 31% of detection delay and increases as much as 25% of detectability compared with the random independent scheme.     

A novel relay-aided centralized cooperative spectrum sensing scheme using radio frequency energy harvesting

In a cooperative spectrum sensing (CSS) network, the primary signal can be used as a radio frequency (RF) source in order to power the energy-constrained sensor nodes of the secondary network. This work presents a novel hybrid model combining an optimal relay selection scheme to incorporate RF energy harvesting in a centralized CSS network. The secondary users, which are equipped with RF energy harvesting capabilities, act as relays in order to forward the sensing information to a fusion center. Here, we have derived an enhanced multi-relay selection strategy to maximize the end-to-end signal-to-noise ratio of the links. Furthermore, a new voting rule is proposed based on the generalized K-out-of-M rule, such that it minimizes our objective error function. The performance analysis of our proposed model is presented with respect to the flexible relay positions. We have used complementary receiver operating characteristic curves for analyzing the detection performance of the CSS model with our derived voting rule. Simulation results using MATLAB show that the proposed model gives a better detection probability and a smaller error rate than some related existing works.