This paper addresses modeling and analysis of interference in wireless networks. We present a novel clustered mean-field hard-core model with non-homogeneous clusters. We develop a theory for throughput equalization in this model which can serve as a distributed strategy for throughput equalization. We also present stability analysis of the throughput equalization strategy. This work recovers several previously known results as special cases. We present results of Monte-Carlo simulations to evaluate the proposed strategy in mean-field networks.

Intelligent reflecting surfaces (IRS) are surfaces consisting of an array of a large number of passive but individually controlled reflecting elements. This paper explores the use of genetic algorithms for optimal allocation of the reflecting elements in a multi-user setting. In particular, it considers maximizing the sum-rate and equalizing the throughput in a wireless environment consisting of multiple co-located IRSes. A real-coded genetic algorithm for solving this problem is presented. Experimental results suggest that the method is effective.

This paper addresses clock synchronization in networks of wirelessly connected nodes, such as, Internet of Things (IoT) and Industrial Internet of Things (IIoT) networks. Synchronizing wirelessly networked sensor nodes in large deployments poses some unique challenges. Though the Precision Time Protocol (PTP), defined in the IEEE 1588 standard, is becoming the dominant clock synchronization method, its requirements of transparent bridges and specialized network interface cards are at odds with the low-cost devices, whereas, low-cost limited-capability sensor nodes are a practical necessity in large-scale deployments. This paper addresses this gap. It presents Event Reference Synchronization (ERS), an asynchronous low-overhead clock synchronization method for IoT and IIoT networks consisting of predominantly low-cost devices. ERS is capable of providing secure time reference service. Evaluation of ERS on a proof-of-concept testbed was carried out where all its components were implemented on a low-cost IoT platform. ERS achieved 85 microsecond accuracy despite using software timestamping.

In this paper, we propose a maximum dispersion based approach for the problem of determining the feasibility of a set of single-hop rates in SINR model. Recent algorithmic work on capacity maximization has focused on maximizing the number of simultaneously scheduled links. We show that such an approach is not suitable for the problem. We present a polynomial time algorithm to determine the feasibility. We present several simulation results to evaluate the proposed approach. The results confirm that maximum dispersion-based approach is well suited for the problem and that such an approach performs significantly better than the existing work.

In this paper we consider the problem of equalizing throughput of nodes in CSMA-based wireless networks. We model interference in a network using conflict graph, where edges represent hard-core interaction, meaning that the two nodes an edge connects cannot be simultaneously active, or transmitting. In practice, the degrees of nodes in a conflict graph are not constant. In such cases, using CSMA leads to lack of fairness since nodes with larger degree have the potential of getting hold of the medium for smaller fraction of time than the nodes with smaller degree. We present a distributed strategy for throughput equalization. The proposed strategy is based on a mean-field hard-core model of interference, and it equalizes the throughput of the nodes with different degrees. We also show that the mean-field hard-core model exhibits a certain phase transition. We present results of Monte-Carlo simulations to evaluate the the proposed strategy in square grid networks and Poisson networks, in addition to mean-field networks.

In wireless networks that operate in those bands where spectrum sharing occurs across a variety of wireless technologies, such as the license-free Industrial Scientific and Medical (ISM) bands, mitigating interference becomes challenging. Addressing interference is an important aspect for the design and development of solutions intended to satisfy the demands of applications requiring QoS guarantees. In this paper, we investigate dynamic radio resource adaptation techniques based on instantaneous spectrum usage. Using a novel metric to quantify the spectrum usage, we address packet size and error correction code overhead optimizations. On one hand, large payloads lead to energy and throughput gains due to the amortization of the transmission overheads, but on the other hand, larger payloads imply larger resource wastage in the event of packet collisions. Using real-world data, we found that payload size in the neighbourhood of 100 bytes leads to near-optimal performance in general in the IEEE 802.15.4 networks. Our data also shows that for very high interference scenarios, erasure codes capable of correcting 10% of the packet payload can provide an equivalent Signal to Interference plus Noise Ratio (SINR) gain of 25 dB with probability greater than 0.6. This is significant for interference management and for increasing spatial re-use by employing lower transmission power. We show that erasure codes drastically improve energy-efficiency and throughput of low-power wireless links. In the heavy interference regime, even though interference doubles the energy-per-usable-bit cost, erasure codes remain cost-effective for very large payload sizes, up-to 1500 bytes. Finally, we discuss interference-dependent dynamic adjustment of the correction capacity of erasure codes.

We consider reliable communications in Body Area Networks (BAN), where a set of nodes placed on human body are connected using wireless links. In order to keep the Specific Absorption Rate (SAR) as low as possible for health safety reasons, these networks operate in low transmit power regime, which however, is known to be error prone. It has been observed that the fluctuations of the Received Signal Strength (RSS) at the nodes of a BAN on a moving person show certain regularities and that the magnitude of these fluctuations are significant (5 - 20 dB). In this paper, we present BANMAC, a MAC protocol that monitors and predicts the channel fluctuations and schedules transmissions opportunistically when the RSS is likely to be higher. The MAC protocol is capable of providing differentiated service and resolves co-channel interference in the event of multiple co-located BANs in a vicinity. We report the design and implementation details of BANMAC integrated with the IEEE 802.15.4 protocol stack. We present experimental data which show that the packet loss rate (PLR) of BANMAC is significantly lower as compared to that of the IEEE 802.15.4 MAC. For comparable PLR, the power consumption of BANMAC is also significantly lower than that of the IEEE~802.15.4. For co-located networks, the convergence time to find a conflict-free channel allocation was approximately 1 s for the centralized coordination mechanism and was approximately 4 s for the distributed coordination mechanism.

Hexagonal wireless sensor network refers to a network topology where a subset of nodes have six peer neighbors. These nodes form a backbone for multi-hop communications. In a previous work, we proposed the use of hexagonal topology in wireless sensor networks and discussed its properties in relation to real-time (bounded latency) multi-hop communications in large-scale deployments. In that work, we did not consider the problem of hexagonal topology formation in practice - which is the subject of this research. In this paper, we present a decentralized algorithm that forms the hexagonal topology backbone in an arbitrary but sufficiently dense network deployment. We implemented a prototype of our algorithm in NesC for TinyOS based platforms. We present data from field tests of our implementation, collected using a deployment of fifty wireless sensor nodes.

Since wireless ad-hoc networks use shared communication medium, accesses to the medium must be coordinated to avoid packet collisions. Transmission scheduling algorithms allocate time slots to the nodes of a network such that if the nodes transmit only during the allocated time slots, no collision occurs. For real-time applications, by ensuring deterministic channel access, transmission scheduling algorithms have the added significance of making guarantees on transmission latency possible. In this paper we present a distributed transmission scheduling algorithm for hexagonal wireless ad-hoc networks with a particular focus on Wireless Sensor Networks. Afforded by the techniques of ad-hoc networks topology control, hexagonal meshes enable trivial addressing and routing protocols. Our transmission scheduling algorithm constructs network-wide conflict-free packet transmission schedule for hexagonal networks, where the overhead of schedule construction in terms of message exchanges is zero above and beyond that for topology control and other network control related functions. Furthermore, the schedule is optimal in the sense that the bottleneck node does not idle. We also present an implicit clock synchronization algorithm to facilitate scheduling. We derive the real time capacity of our scheduling algorithm. We present evaluations of our scheduling algorithm in the presence of topological irregularities using simulation.

Wireless sensor networks hold a very promising future. The nodes of wireless sensor networks (WSN) have a small energy supply and limited bandwidth available. Since radio communication is expensive in terms of energy consumption, the nodes typically spend most of their energy reserve on wireless communication (rather than on CPU processing) for data dissemination and retrieval. Therefore, the role of energy conserving data communication protocols and services in WSN can not be overemphasized. Caching data at locations that minimize packet transmissions in the network reduces the power consumption in the network, and hence extends its lifetime. Finding locations of the nodes for caching data to minimize communication cost corresponds to finding the nodes of a weighted Minimum Steiner tree whose edge weights depend on the edge's Euclidean length and its data traffic rate. We call this tree a Steiner Data Caching Tree (SDCT). We prove that an optimal SDCT is binary, and that at-least two of the three internal angles formed at the Steiner points are equal. We derive expressions that determine the exact location of a Steiner point for a set of three nodes based on their location and their data refresh rate requirements. Based on these (optimality) results, we present a dynamic distributed energy-conserving application-layer service for data caching and asynchronous multicast. We present the results of simulation of our service that verifies its power saving properties.

Multihop wireless sensor networks have recently emerged as an important embedded computing platform. This paper defines a quantitative notion of real-time capacity of a wireless network. Real-time capacity describes how much real-time data the network can transfer by their deadlines. A capacity bound is derived that can be used as a sufficient schedulability condition for a class of fixed-priority packet scheduling algorithms. Using this bound, a designer can perform capacity planning prior to network deployment to ensure satisfaction of applications' real-time requirements.

In recent years, large distributed sensor networks have emerged as a new fast-growing application domain for wireless computing. In this paper, we present a distributed application-layer service for data placement and asynchronous multicast whose purpose is power conservation. Since the dominant traffic in a sensor network is that of data retrieval, (i) caching mutable data at locations that minimize the sum of request and update traffic, and (ii) asynchronously multicasting updates from sensors to observers can significantly reduce the total number of packet transmissions in the network. Our simulation results show that our service subsequently reduces network energy consumption while maintaining the desired data consistency semantics.