Health monitoring of dairy cattle plays a vital role for increasing the dairy products supply worldwide. Nowadays farmers are showing less interest in dairy sector as animals are suffering from various ailing health issues, unpredictable killing diseases, and advanced breeding costs. Therefore, it is necessary for farmers to adopt efficient technical methods for cattle health monitoring to increase the milk production supply. This study documented various wireless sensor network (WSN)-based automatic health monitoring systems for monitoring various diseases of dairy cattle. The main objective of WSN-based intelligent monitoring systems in farm automation is to monitor the health of dairy cattle on regular basis. This monitoring system needs to be installed in local and remote locations of farms that will assist the concerned farmers in monitoring their cattle activities from diverse locations for the whole day. All collected factors from the automated system will be stored in a database. Subsequently, with the help of farm automation, farmers can retrieve information for the execution of correct farm control strategies. Moreover, WSN is low-cost technology which is specific for the identification of diseases in dairy animals. This revolution in advanced technological farm automation will aid in improving the productivity rate with the reduction of human intervention. This review study concludes all cattle monitoring systems along with various issues and challenges.

Device-free indoor localisation based on received signal strength (RSS) is unobtrusive and cheap. In a world where most environments are rich in ubiquitous small radio transmitters, it has the potential of being used in a ‘parasitic’ way, by exploiting the transmissions for localisation purposes without any need for additional hardware installation. Starting from state of the art, several steps are needed to reach this aim, the first of which are tackled in this study. The most promising algorithms from the literature are used to experiment in a real-world environment and with a rigorous measurement and analysis framework. Their positioning error performance is analysed versus number and position of devices. The original results obtained show that the currently available RSS-based device-free indoor localisation methods may be well suited to serve as a basis for providing a cheap localisation service in smart environments rich in Internet of things radio devices.

Localisation of sensor nodes in a wireless sensor network (WSN) is an important problem in various applications like cyber-physical systems, Internet of things and context aware pervasive systems. Cooperative localisation using multidimensional scaling (MDS) has been used successfully in many localisation applications. A primary requirement in MDS is the computation of accurate distance estimates between pairs of nodes in a WSN. However, the estimated distances are erroneous in MDS especially for node pairs that are connected using multiple hops. This leads to an overall increase in error of location estimates. A recent development in social networks called small world phenomena can be introduced in a WSN leading to small world WSN (SW-WSN). SW-WSN exhibits low average path length and high average clustering coefficient and yields accurate distance estimates between pairs of nodes. In this work, a novel cooperative localisation method that uses heterogeneous nodes (H-nodes) is proposed over SW-WSN. In addition, two optimal H-node allocation methods are also developed for the cooperative localisation method. The significance of the proposed method in reducing localisation error, energy consumption, and bandwidth requirement is illustrated by simulations and extensive experiments on a real WSN testbed.

In wireless sensor networks (WSNs), network lifetime and energy consumption are two important parameters which directly impacts each other. In order to enhance the global network lifetime, one should need to utilise the available sensors’ energy in an optimise way. There are several approaches discussed in the literature to maximise the network lifetime for well-known target coverage problem in WSN. The target coverage problem is presented as a maximum network lifetime problem (MLP) and solved heuristically using various approaches. In this study, the authors propose a genetic algorithm (GA)-based meta-heuristic to solve the above said MLP. The GA is a non-linear optimisation solution method which is proven to be better as compared to the column generation or approximation schemes.

This study attempts to deduce the optimal hops in a multi-hop wireless sensor network (WSN) ensuring power-efficient communication under signal-to-interference noise ratio (SINR) constraints. The authors model the total route power consumption under wireless channel quality constraints. WSNs usually employ a multiple-source, single-sink structure for information transfer, necessitating data aggregation. The authors calculate optimal hops for three different data aggregation schemes. They further propose a robust hybrid data aggregation strategy that demonstrates an improved performance with respect to minimal power consumption when compared with employing individual data aggregation schemes. The data aggregation scheme employing optimal hops has been implemented and results have been verified for an illustrative application in water supply and drainage monitoring system. The authors have formulated the problem as a standard convex optimisation problem. They employ Karush–Kuhn–Tucker optimality conditions to derive the analytical expressions of the globally optimal hops, which incorporate the influence of route power, receiver noise, and SINR thresholds. The transmit power and hop count are varied simultaneously until an optimised hop count is achieved. The relation between the node transmit power and the number of hops in the route is derived and the condition for feasible optimal hops is presented.