Covert communication conceals transmission of messages between Alice and Bob from an adversary, Willie, who tries to determine if a transmission took place or not. While covert communication in a basic, standard setting where all variables are known to Willie, results in the well-known square-root law, when a jammer is present and assists Alice by creating uncertainty in Willie's decoder, a strictly positive transmission rate is possible. In this work, we analyze the case where the jammer is equipped with multiple antennas. Specifically, we analyze the effect of multiple antennas at the jammer on Alice's transmission power and consequently on the transmission rate. We consider both the case where the channel knowledge of Willie is known as well as the case where it is unknown. We formulate several optimization problems for the transmission strategies of the jammer to maximize his assistance to Alice, in terms of maximizing Bob's received SNR and consequently the covert rate. When the channel information is known to the jammer, we show that under an achievable covertness scheme, the optimal strategy of the jammer is to perform beamforming towards a single direction with all his available power. This direction though, is not trivial, since it reflects a tradeoff point between minimizing the interference at Bob and maximizing the interference at Willie. When the channel knowledge is unknown, we show that the optimal strategy of the jammer is either to transmit isotropically to all directions or to the null-space of Bob, where this choice depends on certain channel conditions. This is in contrast to current schemes in the literature. Furthermore, we extend the optimization problems to the case where Bob is also equipped with multiple antennas, and provide insightful results, shown to be asymptotically optimal, accompanied by simulations.

Wireless sensor networks (WSNs) have taken a giant leap in scale, expanding their applicability to a large variety of technological domains and applications, ranging from the Internet of things (IoT) for smart cities and smart homes to wearable technology healthcare applications, underwater, agricultural and environmental monitoring and many more. This expansion is rapidly growing every passing day in terms of the variety, heterogeneity and the number of devices which such applications support. Data collection is commonly the core application in WSN and IoT networks, which are typically composed of a large variety of devices, some constrained by their resources (e.g., processing, storage, energy) and some by highly diverse demands. Many challenges span all the conceptual communication layers, from the Physical to the Applicational. Many novel solutions devised in the past do not scale well with the exponential growth in the population of the devices and need to be adapted, revised, or new innovative solutions are required to comply with this massive growth. Furthermore, recent technological advances present new opportunities which can be leveraged in this context. This paper provides a cross-layer perspective and review of data gathering in WSN and IoT networks. We provide some background and essential milestones that have laid the foundation of many subsequent solutions suggested over the years. We mainly concentrate on recent state-of-the-art research, which facilitates the scalable, energy-efficient, cost-effective, and human-friendly functionality of WSNs and the novel applications in the years to come.

Data collection in Wireless Sensor Networks (WSN) draws significant attention, due to emerging interest in technologies ranging from Internet of Things (IoT) networks to simple “Presence” applications, which identify the status of the devices (active or inactive). Numerous Medium Access Control (MAC) protocols for WSN, which can address the challenge of data collection in dense networks, were suggested over the years. Most of these protocols utilize the traditional layering approach, in which the MAC layer is unaware of the encapsulated packet payload, and therefore there is no connection between the data collected, the physical layer and the signaling mechanisms. Nonetheless, in many of the applications that intend to utilize such protocols, nodes may need to exchange very little information, and do so only sporadically, that is, while the number of devices in the network can be very large, only a subset wishes to transmit at any given time. Thus, a tailored protocol, which matches the signaling, physical layer and access control to traffic patterns is required. In this work, we design and analyze a data collection protocol based on information theoretic principles. In the suggested protocol, the sink collects messages from up to K sensors simultaneously, out of a large population of sensors, without knowing in advance which sensors will transmit, and without requiring any synchronization, coordination or management overhead. In other words, neither the sink nor the other sensors need to know who are the actively transmitting sensors, and this data is decoded directly from the channel output. We provide a simple codebook construction with very simple encoding and decoding procedures. We further design a secure version of the protocol, in which an eavesdropper observing only partial information sent on the channel cannot gain significant information on the messages transmitted or even which are the sources that sent these messages.

This study introduces a novel approach to data gathering in energy-harvesting wireless sensor networks (EH-WSNs) utilizing cooperative multi-agent reinforcement learning (MARL). In addressing the challenges of efficient data collection in resource-constrained WSNs, we propose and examine a decentralized, autonomous communication framework where sensors function as individual agents. These agents employ an extended version of the Q-learning algorithm, tailored for a multi-agent setting, enabling independent learning and adaptation of their data transmission strategies. We introduce therein a specialized $ϵ$-p-greedy exploration method which is well suited for MAS settings. The key objective of our approach is the maximization of report flow, aligning with specific applicative goals for these networks. Our model operates under varying energy constraints and dynamic environments, with each sensor making decisions based on interactions within the network, devoid of explicit inter-sensor communication. The focus is on optimizing the frequency and efficiency of data report delivery to a central collection point, taking into account the unique attributes of each sensor. Notably, our findings present a surprising result: despite the known challenges of Q-learning in MARL, such as non-stationarity and the lack of guaranteed convergence to optimality due to multi-agent related pathologies, the cooperative nature of the MARL protocol in our study obtains high network performance. We present simulations and analyze key aspects contributing to coordination in various scenarios. A noteworthy feature of our system is its perpetual learning capability, which fosters network adaptiveness in response to changes such as sensor malfunctions or new sensor integrations. This dynamic adaptability ensures sustained and effective resource utilization, even as network conditions evolve. Our research lays grounds for learning-based WSNs and offers vital insights into the application of MARL in real-world EH-WSN scenarios, underscoring its effectiveness in navigating the intricate challenges of large-scale, resource-limited sensor networks.

Time synchronization is critical in distributed environments. A variety of network protocols, middleware and business applications rely on proper time synchronization across the computational infrastructure and depend on the clock accuracy. The Network Time Protocol (NTP) is the current widely accepted standard for synchronizing clocks over the Internet. NTP uses a hierarchical scheme in order to synchronize the clocks in the network. In this paper we present a novel non-hierarchical peer-to-peer approach for time synchronization termed CTP-Classless Time Protocol. This approach exploits convex optimization theory in order to evaluate the impact of each clock offset on the overall objective function. We define the clock offset problem as an optimization problem and derive its optimal solution. Based on the solution we develop a distributed protocol that can be implemented over a communication network, prove its convergence to the optimal clock offsets and show its properties. For compatibility, CTP may use the packet format and number of measurements used by NTP. We also present methodology and numerical results for evaluating and comparing the accuracy of time synchronization schemes. We show that the CTP outperforms hierarchical schemes such as NTP in the sense of clock accuracy with respect to a universal clock

We address a cost optimization problem of running instances of applications in a remote cloud configuration of multiple virtual machines (VMs). Managing VMs involves a sophisticated trade-off between demands, which are expressed by the internal provisional costs of leased cloud resources, and exogenous costs expressed by service revenues that are typically bound to SLAs. The internal costs may include VM deployment/termination cost, and VM lease cost. The exogenous costs include rewards accumulated due to accomplished tasks. In the case where the SLA restricts the performance level, tasks incoming at VMs that reached that level are rejected, causing fines deducted against the rewards. The performance level is also quantified, namely, by means of a delay cost. We model this problem by cost-optimal load balancing to a queuing system with a flexible number of queues. We analyze the system by Markov decision process (MDP) and numerically solve it to find the optimal policy, which captures the aforementioned costs and performance constraints. Within this constrained framework, we also investigate the impact of average VM deployment time. We show that the optimal policy possesses decision thresholds which depend on several parameters. We validate policies found by MDP through a set-up implemented on AWS.