In this paper we propose a new dual radio IoT network architecture for wildlife monitoring system (WMS). WMS leverages bluetooth low energy (BLE) in low power wide area networks (LPWANs) by dynamically changing the operating radio based on the proximity among herd of wild animals. This approach will facilitate ultra-low power IoT devices to be deployed for sustainable wildlife monitoring application. In addition we present an analytical model to investigate the performance of the proposed IoT network in terms of energy consumption under a wildlife monitoring use-case. The simulation results show that the dual radio network leads to a higher energy efficiency when compared to the network utilizing only LPWAN. Moreover, our network readily doubles the network life time for various data traffic rates.

Abstract-In this paper we introduce an opportunistic dual radio IoT network architecture for wildlife monitoring systems (WMS). Since data processing consumes less energy than transmitting the raw data, the proposed architecture leverages opportunistic mobile networks in a fixed LPWAN IoT network infrastructure. This solution will facilitate an IoT devices to be deployed for ultra-low power and sustainable wildlife monitoring applications. As part of the IoT infrastructure, a LoRa based network is presented with coverage characterization and preliminary test bed deployment for wildlife tracking purpose. In addition, through simulation, the utilization of existing BLE based opportunistic data collection protocols for the proposed architecture is investigated.

LoRa is an emerging wireless standard specifically designed for Low Power Wide Area Networks (LPWANs). It provides long range, low data rate, and energy efficient wireless communication and is believed to have high potential for realization of a large number of Internet of Things (IoT) applications. Various research papers have already reported on the performance analysis of LoRaWAN protocol in terms of radio communication range, and reliability for outdoor environments, while performance analysis for indoor environments have not yet received enough attention. In this paper, we provide an in-depth performance evaluation of LoRa for indoor IoT applications.

Between 1960 and 1990, 95% of the black rhino population in the world was killed. In South Africa, a rhino was killed every 8 h for its horn throughout 2016. Wild animals, rhinos and elephants, in particular, are facing an ever increasing poaching crisis. In this paper, we review poaching detection technologies that aim to save endangered species from extinction. We present requirements for effective poacher detection and identify research challenges through the survey. We describe poaching detection technologies in four domains: perimeter based, ground based, aerial based, and animal tagging based technologies. Moreover, we discuss the different types of sensor technologies that are used in intruder detection systems such as: radar, magnetic, acoustic, optic, infrared and thermal, radio frequency, motion, seismic, chemical, and animal sentinels. The ultimate long-term solution for the poaching crisis is to remove the drivers of demand by educating people in demanding countries and raising awareness of the poaching crisis. Until prevention of poaching takes effect, there will be a continuous urgent need for new (combined) approaches that take up the research challenges and provide better protection against poaching in wildlife areas.

Abstract-In this paper, we introduce dual radio based IoT network architecture for wildlife monitoring system (WMS). This solution will facilitate an IoT devices to be deployed for sustainable wildlife monitoring applications. In addition we present MANER, a managed data dissemination scheme for WMS. In MANER, data forwarding is optimized with a replication function to control and prioritize data dissemination. In WMS scenario wild animals show a sparsely con-specific mobility, which often results in a sporadic wireless link among nodes. Unlike existing opportunistic algorithms, MANER optimally makes forwarding decisions by leveraging locally available information. Hence, the proposed algorithm adopts to dynamic network topology due to the inherent intermittent connectivity among mobile herd of animals. We evaluated the performance of MANER by considering standard and real-life mobility models. Experimental results indicated that MANER decreases the average latency by up-to 65%, when compared to benchmark opportunistic algorithms. In addition MANER readily increased the network delivery ratio for various data traffic rates.