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This paper aims to review existing wireless sensor network (WSN) setups in various domains, focusing on affordable WSN so that it can be effectively utilised in solving the environmental problems. WSN is being explored in many applications such as home security, smart spaces, environmental monitoring, battlefield surveillance and target tracking. WSN consists of a number of tiny, low-powered, energy-constrained sensor nodes with sensing, data processing and wireless communication components. Creating a WSN setup will make the monitoring system effective and in future, it will give a roadmap for solving some common societal problems.

Various research papers in the area of WSN have been reviewed on the basis of technologies and application on different fields.

WSN was found to be the most effective solution in areas which are less explored due their hazardous nature and are difficult to reach.

This review is based on research papers available and a recent trend in the area of WSN has been explored.

In particular, this paper aims to systematically analyze a few prominent wireless sensor network (WSN) clustering routing protocols and compare these different approaches according to the taxonomy and several significant metrics.

In this paper, the authors have summarized recent research results on data routing in sensor networks and classified the approaches into four main categories, namely, data-centric, hierarchical, location-based and quality of service (QoS)-aware, and the authors have discussed the effect of node placement strategies on the operation and performance of WSNs.

Performance-controlled planned networks, where placement and routing must be intertwined and everything from delays to throughput to energy requirements is well-defined and relevant, is an interesting subject of current and future research. Real-time, deadline guarantees and their relationship with routing, mac-layer, duty-cycles and other protocol stack issues are interesting issues that would benefit from further research.

The purpose of this paper is to investigate conditional and unconditional lifetime sequence of wireless sensor networks (WSN) that have many important practical applications. A significant limitation for WSN is its short lifetime due to the limited capacity of the battery. Renewable energy can significantly extend the lifetime of WSN. In this paper, we investigate the whole sequence of lifetimes of every sensor in WSN, as different application scenarios have different requirement on how many sensors can die until the WSN is no longer functional.

Linear programming formulation was used to investigate both the conditional and unconditional lifetime sequence of WSN. The lifetime sequences of WSN without and with differ levels of solar power were studied.

This investigation of lifetime sequences discovered three interesting phenomena: the sensors that die first are on the peripheral of the network, rather close to the base station; multiple sensors tend to die simultaneously; and the lifetimes of sensors that die later can be extended by renewable energy much more significantly than those that die early, which is very good news to applications that can tolerate the death of a fraction of sensors.

In this paper, the first optimization formulation for maximizing both unconditional and conditional lifetime sequences of WSNs with renewable energy sources was provided. Only the conditional lifetime sequence has been investigated in a previous work, but this method runs n-times faster than the previous work, with n being the number of nodes in the WSN.

The paper aims to investigate performance benefits associated with adopting a mobile wireless sensor network (WSN). Sensor nodes are generally energy constrained due to the latter being acquired from onboard battery cells. If one or more sensor nodes fail, possible coverage holes may be created which could invariantly lead to a reduced network lifetime. The paper proposes that instead of rendering the entire WSN inoperative, sensor nodes should physically change position within the region of interest thus adaptively altering the WSN topology with a view of recovering from failures. This type of motion will be referred to as “self healing”.

This paper presents a mobility scheme based on Bayesian networks for predictive reasoning (BayesMob) which is essentially a distributed self healing algorithm for coordinating physical relocation of sensor nodes. Using the algorithm, sensor nodes can predict the performance of the WSN in terms of coverage given that the node moves in a given direction. The evidence for this hypothesis is acquired from local neighborhood information.

The paper compares BayesMob with an alternative algorithm – Coverage Fidelity Algorithm – and shows that BayesMob maintains a higher level WSN coverage for a greater percentage of failures, thus increasing the useful lifetime of the WSN.

The physical relocation of sensor nodes will incur energy overhead, therefore the tradeoffs between all application criteria should be investigated before implementation.

This paper presents a Bayesian network based motion coordination algorithm for WSN which repairs coverage holes caused by energy exhaustion and/or abrupt node failures.

Load balancing is an effective enhancement to the proposed routing protocol, and the basic idea is to share traffic load among cluster members to reduce the dropping probability due to queue overflow at some nodes. This paper aims to propose a novel hierarchical approach called distributed energy efficient adaptive clustering protocol (DEACP) with data gathering, load-balancing and self-adaptation for wireless sensor network (WSN). The authors have proposed DEACP approach to reach the following objectives: reduce the overall network energy consumption, balance the energy consumption among the sensors and extend the lifetime of the network, the clustering must be completely distributed, the clustering should be efficient in complexity of message and time, the cluster-heads should be well-distributed across the network, the load balancing should be done well and the clustered WSN should be fully connected. Simulations show that DEACP clusters have good performance characteristics.

A WSN consists of large number of wireless capable sensor devices working collaboratively to achieve a common objective. One or more sinks [or base stations (BS)] which collect data from all sensor devices. These sinks are the interface through which the WSN interacts with the outside world. Challenges in WSN arise in implementation of several services, and there are so many controllable and uncontrollable parameters (Chirihane, 2015) by which the implementation of WSN is affected, e.g. energy conservation. Clustering is an efficient way to reduce energy consumption and extend the life time of the network, by performing data aggregation and fusion to reduce the number of transmitted messages to the BS (Chirihane, 2015). Nodes of the network are organized into the clusters to process and forwarding the information, while lower energy nodes can be used to sense the target, and DEACP makes no assumptions on the size and the density of the network. The number of levels depends on the cluster range and the minimum energy path to the head. The proposed protocol reduces the number of dead nodes and the energy consumption, to extend the network lifetime. The rest of the paper is organized as follows: An overview of related work is given in Section 2. In Section 3, the authors propose an energy efficient level-based clustering routing protocol (DEACP). Simulations and results of experiments are discussed in Section 4. In Section 5, the authors conclude the work presented in this paper and the scope of further extension of this work.

The authors have proposed the DEACP approach to reach the following objectives: reduce the overall network energy consumption, balance the energy consumption among the sensors and extend the lifetime of the network, the clustering must be completely distributed, the clustering should be efficient in complexity of message and time, the cluster-heads should be well-distributed across the network, the load balancing should be done well, the clustered WSN should be fully connected. Simulations show that DEACP clusters have good performance characteristics.

Wireless sensor network (WSN) and mobile crowd sensing (MCS) technologies face some challenges, especially when deployed in a large environment such as a smart city environment. WSN faces network latency, packets delivery and limited lifetime due to the nature of the used constrained internet of things small devices and low power network. On the other hand, most of the current applications that adapt MCS technology use 3G or long term evalution network to collect data and send them directly to the server. This leads to higher battery and bandwidth consumption and higher data cost.

This paper proposes a hybrid routing protocol based on the routing protocol (RPL) protocol that combines the two wireless sensing technologies (WSN and MCS) and allows the integration between them. The aim is to use MCS nodes in an opportunistic way to support static WSN nodes to enhance the performance.

The evaluation of the proposed protocol was conducted in a static WSN to study the impact of the integration on the WSN performance. The results reveal a good enhancement on packet delivery ratio (17% more), end-to-end delay (50% less) and power consumption (25% less) compared with native RPL (without MCS integration).

The authors believe that the hybrid-RPL protocol can be useful for sensing and data collection purposes, especially in urban areas and smart city contexts.

Localization is a fundamental problem in wireless sensor networks. In many applications, sensor location information is critical for data processing and meaning. While the global positioning system (GPS) can be used to determine mote locations with meter precision, the high hardware cost and energy requirements of GPS receivers often prohibit the ubiquitous use of GPS for location estimates. This high cost (in terms of hardware price and energy consumption) of GPS has motivated researchers to develop localization protocols that determine mote locations based on cheap hardware and localization algorithms. The purpose of this paper is to present a comprehensive review of wireless sensor network localization techniques, and provide a detailed overview for several distance‐based localization algorithms.

To provide a detailed summary of wireless sensor network localization algorithms, the authors outline a tiered classification system in which they first classify algorithms as distributed, distributed‐centralized, or centralized. From this broad classification, the paper then further categorizes localization algorithms using their protocol techniques. By utilizing this classification system, the authors are able to provide a survey of several wireless sensor network localization algorithms and summarize relative algorithm performance based on the algorithms' classification.

There are numerous localization algorithms available and the performance of these algorithms is dependent on network configuration, environmental variables, and the ranging method implemented. When selecting a localization algorithm, it is important to understand basic algorithm operation and expected performance. This tier‐based algorithm classification system can be used to gain a high‐level understanding of algorithm performance and energy consumption based on known algorithm characteristics.

Localization is a widely researched field and given the quantity of localization algorithms that currently exist, it is impossible to present a complete review of every published algorithm. Instead, the paper presents a holistic view of the current state of localization research and a detailed review of ten representative distance‐based algorithms that have diverse characteristics and methods. This review presents a new classification structure that may help researchers understand, at a high‐level, the expected performance and energy consumption of algorithms not explicitly addressed by our work.

Since communication usually accounts as the foremost problem for power consumption, there are some approaches, such as topology control and network coding (NC), for diminishing the activity of sensors’ transceivers. If such approaches are employed simultaneously, then the overall performance does raise as expected. In a wireless sensor network (WSN), the linear NC has been shown to enhance the performance of network throughput and reduce delay. However, the NC condition of existing NC-aware routings may experience the issue of false-coding effect in some scenarios and usually neglect node energy, which highly affects the energy efficiency performance. The purpose of this paper is to propose a new NC scheduling in a WSN with the intention of maximizing the throughput and minimizing the energy consumption of the network.

The improved meta-heuristic algorithm called the improved mutation-based lion algorithm (IM-LA) is used to solve the problem of NC scheduling in a WSN. The main intention of implementing improved optimization is to maximize the throughput and minimize the energy consumption of the network during the transmission from the source to the destination node. The parameters like topology and time slots are taken for optimizing in order to obtain the concerned objective function. While solving the current optimization problem, it has considered a few constraints like timeshare constraint, data-flow constraint and domain constraint. Thus, the network performance is proved to be enhanced by the proposed model when compared to the conventional model.

When 20 nodes are fixed for the convergence analysis, performed in terms of multi-objective function, it is noted that during the 400th iteration, the proposed IM-LA was 10.34, 13.91 and 50% better than gray wolf algorithm (GWO), firefly algorithm (FF) and particle swarm optimization (PSO), respectively, and same as LA. Therefore, it is concluded that the proposed IM-LA performs extremely better than other conventional methods in minimizing the cost function, and hence, the optimal scheduling of nodes in a WSN in terms of the multi-objective function, i.e. minimizing energy consumption and maximizing throughput using NC has been successfully done.

This paper adopts the latest optimization algorithm called IM-LA, which is used to solve the problem of network coding scheduling in a WSN. This is the first work that utilizes IM-LA for optimal network coding in a WSN.

Wireless sensor networks (WSNs) are periodically collecting data through randomly dispersed sensors (motes), which typically consume high energy in radio communication that mainly leans on data transmission within the network. Furthermore, dissemination mode in WSN usually produces noisy values, incorrect measurements or missing information that affect the behaviour of WSN. In this article, a Distributed Data Predictive Model (DDPM) was proposed to extend the network lifetime by decreasing the consumption in the energy of sensor nodes. It was built upon a distributive clustering model for predicting dissemination-faults in WSN. The proposed model was developed using Recursive least squares (RLS) adaptive filter integrated with a Finite Impulse Response (FIR) filter, for removing unwanted reflections and noise accompanying of the transferred signals among the sensors, aiming to minimize the size of transferred data for providing energy efficient. The experimental results demonstrated that DDPM reduced the rate of data transmission to ∼20%. Also, it decreased the energy consumption to 95% throughout the dataset sample and upgraded the performance of the sensory network by about 19.5%. Thus, it prolonged the lifetime of the network.

Wireless sensor networks (WSN) have been widely adopted for various applications due to their properties of pervasive computing. It is necessary to prolong the WSN lifetime; it avails its benefit for a long time. WSN lifetime may vary according to the applications, and in most cases, it is considered as the time to the death of the first node in the module. Clustering has been one of the successful strategies for increasing the effectiveness of the network, as it selects the appropriate cluster head (CH) for communication. However, most clustering protocols are based on probabilistic schemes, which may create two CH for a single cluster group, leading to cause more energy consumption. Hence, it is necessary to build up a clustering strategy with the improved properties for the CH selection. The purpose of this paper is to provide better convergence for large simulation space and to use it for optimizing the communication path of WSN.

This paper plans to develop a new clustering protocol in WSN using fuzzy clustering and an improved meta-heuristic algorithm. The fuzzy clustering approach is adopted for performing the clustering of nodes with respective fuzzy centroid by using the input constraints such as signal-to-interference-plus-noise ratio (SINR), load and residual energy, between the CHs and nodes. After the cluster formation, the combined utility function is used to refine the CH selection. The CH is determined based on computing the combined utility function, in which the node attaining the maximum combined utility function is selected as the CH. After the clustering and CH formation, the optimal communication between the CH and the nodes is induced by a new meta-heuristic algorithm called Fitness updated Crow Search Algorithm (FU-CSA). This optimal communication is accomplished by concerning a multi-objective function with constraints with residual energy and the distance between the nodes. Finally, the simulation results show that the proposed technique enhances the network lifetime and energy efficiency when compared to the state-of-the-art techniques.

The proposed Fuzzy+FU-CSA algorithm has achieved low-cost function values of 48% to Fuzzy+Particle Swarm Optimization (PSO), 60% to Fuzzy+Grey Wolf Optimizer (GWO), 40% to Fuzzy+Whale Optimization Algorithm (WOA) and 25% to Fuzzy+CSA, respectively. Thus, the results prove that the proposed Fuzzy+FU-CSA has the optimal performance than the other algorithms, and thus provides a high network lifetime and energy.

For the efficient clustering and the CH selection, a combined utility function was developed by using the network parameters such as energy, load, SINR and distance. The fuzzy clustering uses the constraint inputs such as residual energy, load and SINR for clustering the nodes of WSN. This work had developed an FU-CSA algorithm for the selection of the optimal communication path for the WSN.