Due to recent technological advancements in the area of unmanned aerial systems, equipping an unmanned aerial vehicle (UAV) with a base station (BS) has been proposed to augment terrestrial base stations and to enhance the performance of 5G and beyond-5G networks. In this paper, we investigate the 3D placement problem for multiple UAV-BSs that maximizes the number of covered users with the same as well as with different quality-of-service (QoS) requirements. First, we present a mathematical formulation of the multiple UAV-BS placement problem, and show that it is a non-convex optimization problem. Then, we propose two heuristic algorithms, and we show that for users with the same as well as with different QoS requirements, the proposed algorithms outperform the Linear Approximation (LA) state-of the-art algorithm in terms of the average number of covered users and execution time, and achieve near optimal performance. Finally, a tradeoff between the two heuristic algorithms in terms of the average number of covered users and their execution time is presented.

As the number of mobile devices that natively support ad hoc communication protocols increase, large ad hoc networks can be created not only to facilitate communication among the mobile devices, but also to assist devices that are executing computationally intensive applications. Prior work has developed computation offloading systems for mobile devices, but this work has focused exclusively on offloading to single hop neighbors, due in part to the practical challenges of setting up multi-hop networks using existing ad hoc communication protocols. However, limiting the offloading of computation to one-hop neighbors inherently restricts the number of devices that can participate in the distributed computation. By presenting a heuristic, aimed at avoiding partitioning the network, as well as an iterative task assignment algorithm that can optimize the assignment of computational tasks to devices in a multi-hop cooperative network, we are able to evaluate the effect of computational offloading in multi-hop networks. Experimental results, obtained from an implementation on Android devices, are integrated with an analytical model that enables the evaluation of system performance under a variety of conditions. These experimental and analytic results demonstrate the benefit of enabling computation offloading to all devices in a multi-hop cooperative network.

Wireless networks are vital for supporting a range of applications, and energy efficiency is a key factor in improving the operational lifetime for these networks. In multiantenna wireless networks, the energy optimization problem can be addressed using the trade-off between the transmit power and the circuit energy consumption. Multiple-Input Multiple-Output (MIMO) communication is a promising approach that can be efficiently used to reduce the energy consumption for communication. In MIMO systems, the transmit power is spread among more than one antenna, which results in having a high power gain and better spectral efficiency. In this paper, we propose optimizing the MIMO communication within a cluster-based protocol for wireless networks in which the nodes have multiple antennas and may be powered by energy harvesting. The proposed approach adjusts the number of antennas for communication between a normal node and a cluster-head dynamically in order to improve the overall energy efficiency of the network. We evaluate the proposed approach in two scenarios, a network with static nodes with non-rechargeable batteries, and a network with mobile nodes powered through energy harvesting. In both scenarios, the simulation results show that the proposed approach of multi-antenna optimization outperforms a traditional fixed approach in terms of number of received packets, percentage of dead nodes, and energy consumption.

The potential benefits of Mobile Ad Hoc Networks (MANETs) have led to the development of many protocols in order to control and optimize such networks. However, the automatic creation and evolution of ad hoc networks has yet to be exploited. A novel ad hoc protocol named WiFi Direct has been proposed and standardized by the WiFi Alliance with the objective of facilitating the interconnection of nearby devices. WiFi Direct provides high performance direct communication among devices and includes different energy management mechanisms. However, the current WiFi Direct implementations require user interaction for setting up and maintaining the connection. In this work, we exploit redundancy to enable the automatic and fast reconfiguration of WiFi Direct networks in response to network dynamics. We propose a proactive solution to unforeseen group owner failures in order to minimize the packet loss and network discontinuity time by setting up a redundant group on a second virtual network interface. Through emulation on Mininet-WiFi, we find that the proposed redundant scheme substantially decreases packet loss, providing almost continuous connectivity among nodes, which cannot be guaranteed through traditional WiFi Direct schemes. This is the first detailed work examining the auspicious potential of using an additional network interface to support network reformation using WiFi Direct.

Wireless Sensor Networks (WSNs) are crucial in supporting continuous environmental monitoring, where sensor nodes are deployed and must remain operational to collect and transfer data from the environment to a base-station. However, sensor nodes have limited energy in their primary power storage unit, and this energy may be quickly drained if the sensor node remains operational over long periods of time. Therefore, the idea of harvesting ambient energy from the immediate surroundings of the deployed sensors, to recharge the batteries and to directly power the sensor nodes, has recently been proposed. The deployment of energy harvesting in environmental field systems eliminates the dependency of sensor nodes on battery power, drastically reducing the maintenance costs required to replace batteries. In this article, we review the state-of-the-art in energy-harvesting WSNs for environmental monitoring applications, including Animal Tracking, Air Quality Monitoring, Water Quality Monitoring, and Disaster Monitoring to improve the ecosystem and human life. In addition to presenting the technologies for harvesting energy from ambient sources and the protocols that can take advantage of the harvested energy, we present challenges that must be addressed to further advance energy-harvesting-based WSNs, along with some future work directions to address these challenges.

Energy harvesting wireless nodes provide much longer lifetime and higher energy efficiency for wireless networks compared to battery operated systems. In this paper, we study a MIMO wireless communication link in which the nodes are equipped with energy harvesters and rechargeable batteries that are continuously charging from a renewable energy source. Since the harvested energy arrival and thus the future remaining energy of the nodes is not deterministic in practice, we propose a learning approach in order to find the most efficient transmission policy for data communication that maximizes throughput. The problem is formulated as a Markov Decision Process (MDP) with unknown transition probabilities. A Q-Learning approach is proposed to solve the MDP model and find the optimal transmission policy.

The wide diffusion of mobile devices that natively support ad hoc communication technologies has led to a number of protocols for enabling and optimizing Mobile Ad Hoc Networks (MANETs). Nevertheless, the actual utilization of MANETs in real life is still limited, in part due to the lack of protocols for the automatic creation and evolution of ad hoc networks. Recently, a novel ad hoc protocol named WiFi Direct has been proposed and standardized by the WiFi Alliance with the objective of facilitating the interconnection of nearby devices. WiFi Direct provides high performance direct communication among devices, includes different energy management mechanisms, and is now available in most modern mobile devices. However, the current WiFi Direct implementations require user interaction for setting up and maintaining the connection. In this paper, we propose and analyze three practical schemes for creating self-organizing and self-healing WiFi Direct networks using Android OS devices. Experimental results show that our proposed approaches are feasible with different overhead in terms of prior knowledge about the network and coordination between the devices. These techniques provide the first known approaches for the automatic creation and maintenance of MANETs using every day mobile devices.

Water is essential for human survival. Although approximately 71% of the world is covered in water, only 2.5% of this is fresh water; hence, fresh water is a valuable resource that must be carefully monitored and maintained. In developing countries, 80% of people are without access to potable water. Cholera is still reported in more than 50 countries. In Africa, 75% of the drinking water comes from underground sources, which makes water monitoring an issue of key concern, as water monitoring can be used to track water quality changes over time, identify existing or emerging problems, and design effective intervention programs to remedy water pollution. It is important to have detailed knowledge of potable water quality to enable proper treatment and also prevent contamination. In this article, we review methods for water quality monitoring (WQM) from traditional manual methods to more technologically advanced methods employing wireless sensor networks (WSNs) for in situ WQM. In particular, we highlight recent developments in the sensor devices, data acquisition procedures, communication and network architectures, and power management schemes to maintain a long-lived operational WQM system. Finally, we discuss open issues that need to be addressed to further advance automatic WQM using WSNs.

As the number of mobile devices that natively support ad hoc communication protocols increase, large ad hoc networks can be created not only to facilitate communication among the mobile devices, but also to assist devices that are executing computationally intensive applications. Prior work has developed computation offloading systems for mobile devices, but this work has focused exclusively on offloading to single hop neighbors, due in part to the practical challenges of setting up multi-hop networks using existing ad hoc communication protocols. However, limiting the offloading of computation to one-hop neighbors inherently restricts the number of devices that can participate in the distributed computation. In this paper, we propose and evaluate the performance of computational offloading within a multi-hop cooperative network, where mobile devices are able to share the computational load with all other nodes in the network. Additionally, we present an iterative task assignment algorithm that can optimize the assignment of computational tasks to devices in such a multi-hop cooperative network, taking into account the communication overhead of the multi-hop network. Experimental results, obtained from an implementation on Android devices, are integrated with an analytical model that enables the evaluation of system performance under a variety of conditions. These experimental and analytic results demonstrate the benefit of enabling computation offloading to all devices in a multi-hop cooperative network.

In areas where the habitats of elephants and humans are rapidly encroaching on each other, real-time monitoring of the elephants' locations has the potential to drastically improve the co-existence of elephants and humans, resulting in reduced deaths in both groups. However, as tagging (using GPS collars) elephants to obtain such location information is difficult and costly, it is important to ensure very long lifetimes of the tags, which can only be achieved using energy harvesting. In this paper, we present a kinetic energy harvester that uses magnetic levitation and ferro fluid bearings to generate energy from an elephant's movements. In order to determine the feasibility of using this kinetic energy harvester for powering the tags on elephants, we obtained real acceleration data collected from an Asian elephant over a 10 day period, and this data was then used to tune the system to maximize the harvested energy. Using experimentally validated analytical and simulation models, and the actual elephant acceleration data, we find that our prototype can generate 88.91J of energy per day. This energy is not only sufficient to power the tags to acquire and transmit locations 24 times a day to a distance of 114Km (line of sight), but provides a surplus of at least 35.40J, which can be used to increase the frequency of position updates or to support alternative communication options such as GPRS. Therefore, this shows the viability of long-term tracking of elephants.

Power and energy consumption are the most important factors in extending the lifetime of Wireless Sensor Networks (WSN). Many energy efficiency techniques, that consider both the transmission and circuit power consumption have been proposed for the case of Single-Input Single-Output (SISO) WSNs. However, the power consumption of the receiver should also be considered in order to maximize the network lifetime. In this paper, we introduce a novel communication protocol for Multiple-Input Multiple-Output (MIMO) WSNs. In this protocol, the number of antennas to be used at both the transmitter and receiver are selected according to the energy consumption of the scheme, the remaining energy at the nodes, the distance between the nodes, and the target bit error rate. Starting from a policy that selects the optimal number of antennas, we then propose 3 low complexity heuristics with different information requirements. Numerical results show that our proposed communication protocols dramatically outperform the performance of a traditional fixed MIMO system in terms of energy consumption and system lifetime.

Volunteer computing provides a practical and low cost solution to the increasing computational demands of many applications. Recent advancements in mobile device processing capabilities, combined with the energy efficiency of the mobile devices, make their inclusion in a distributed computing architecture particularly appealing. However, the intrinsic requirement of Internet connectivity to participate in volunteer computing limits the direct adoption of mobile devices due to service availability or related costs to connect to the Internet. In this paper, we propose and implement a novel computational architecture that extends the ability of mobile devices to participate in volunteer computing through ad hoc networking. By introducing decentralized task distribution points, mobile devices are invited to join the computation via device to device communication, removing the requirement for an Internet connection. Using a prototype implementation running on Android devices, we investigate the impact of a promising ad hoc communication technology, namely WiFi Direct, and two task distribution algorithms with different computation and communication overheads, under various scenarios. Experimental results show that our proposed approach is feasible with only minor additional energy consumption at the decentralized task distribution points.

Energy-harvesting wireless sensor networking is an emerging technology with applications to various fields such as environmental and structural health monitoring. A distinguishing feature of wireless sensors is the need to perform both source coding tasks, such as measurement and compression, and transmission tasks. It is known that the overall energy consumption for source coding is generally comparable to that of transmission, and that a joint design of the two classes of tasks can lead to relevant performance gains. Moreover, the efficiency of source coding in a sensor network can be potentially improved via distributed techniques by leveraging the fact that signals measured by different nodes are correlated. In this paper, a data-gathering protocol for multihop wireless sensor networks with energy-harvesting capabilities is studied whereby the sources measured by the sensors are correlated. Both the energy consumptions of source coding and transmission are modeled, and distributed source coding is assumed. The problem of dynamically and jointly optimizing the source coding and transmission strategies is formulated for time-varying channels and sources. The problem consists in the minimization of a cost function of the distortions in the source reconstructions at the sink under queue stability constraints. By adopting perturbation-based Lyapunov techniques, a close-to-optimal online scheme is proposed that has an explicit and controllable tradeoff between optimality gap and queue sizes. The role of side information available at the sink is also discussed under the assumption that acquiring the side information entails an energy cost.

In this paper, we present the implementation and performance evaluation of security functionalities at the link layer of IEEE 802.15.4-compliant IoT devices. Specifically, we implement the required encryption and authentication mechanisms entirely in software and as well exploit the hardware ciphers that are made available by our IoT platform. Moreover, we present quantitative results on the memory footprint, the execution time and the energy consumption of selected implementation modes and discuss some relevant tradeoffs. As expected, we find that hardware-based implementations are not only much faster, leading to latencies shorter than two orders of magnitude compared to software-based security suites, but also provide substantial savings in terms of ROM memory occupation, i.e. up to six times, and energy consumption. Furthermore, the addition of hardware-based security support at the link layer only marginally impacts the network lifetime metric, leading to worst-case reductions of just 2% compared to the case where no security is employed. This is due to the fact that energy consumption is dominated by other factors, including the transmission and reception of data packets and the control traffic that is required to maintain the network structures for routing and data collection. On the other hand, entirely software-based implementations are to be avoided as the network lifetime reduction in this case can be as high as 25%.

Cooperative routing has been shown to be an effective technique to improve the throughput/delay performance of multi-hop wireless ad hoc networks. In addition, suitable cooperation selection policies also allow for a reduction of the overall energy expenditure. In a previous study, we proposed a centralized algorithm to obtain optimal cooperation selection policies in multi-hop networks with the aim of minimizing a linear combination of energy and delay costs. In this paper, we look at this problem from a different angle, devising three online and fully distributed algorithms which only exploit local interactions for the selection of the cooperators. The first technique selects at each hop a fixed number of nodes having the minimum distance with respect to the destination. The second one adopts a look-ahead strategy, which selects a fixed number of nodes at each hop, according to their expected advancement toward the destination. The third technique utilizes a more refined look-ahead strategy, which dynamically adjusts the number of nodes that cooperate at each hop. Numerical results are thus presented for the proposed techniques, comparing them against the optimal centralized strategy and competing algorithms from the literature. These results indicate that our techniques improve upon existing distributed approaches and achieve close-to-optimal performance.

In this paper we consider wireless cooperative multihop networks, where nodes that have decoded the message at the previous hop cooperate in the transmission toward the next hop, realizing a distributed space-time coding scheme. Our objective is finding optimal cooperator selection policies for arbitrary topologies with links affected by path loss and multipath fading. To this end, we model the network behavior through a suitable Markov chain and we formulate the cooperator selection process as a stochastic shortest path problem (SSP). Further, we reduce the complexity of the SSP through a novel pruning technique that, starting from the original problem, obtains a reduced Markov chain which is finally embedded into a solver based on focused real time dynamic programming (FRTDP). Our algorithm can find cooperator selection policies for large state spaces and has a bounded (and small) additional cost with respect to that of optimal solutions. Finally, for selected network topologies, we show results which are relevant to the design of practical network protocols and discuss the impact of the set of nodes that are allowed to cooperate at each hop, the optimization criterion and the maximum number of cooperating nodes.