A critical analysis of research potential, challenges and future directives in industrial wireless sensor networks

In recent years, Industrial Wireless Sensor Networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper a detailed discussion on design objectives, challenges and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines and possible hazards in industrial atmosphere are discussed. The paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. The paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs

Energy Harvesting Communication Networks: Online Policies, Temperature Considerations, and Age of Information

This dissertation focuses on characterizing energy management policies for energy harvesting communication networks in the presence of stochastic energy arrivals and temperature constraints. When the energy arrivals are stochastic and are known only causally at the transmitter, we study two performance metrics: throughput and age of information (AoI). When the energy harvesting system performance is affected by the change of the temperature, we consider the throughput metric. When the energy arrivals are stochastic, we study the throughput maximization problem for several network settings. We first consider an energy harvesting broadcast channel where a transmitter serves data to two receivers on the downlink. The battery at the transmitter in which the harvested energy is stored is of finite size. We focus on online transmission schemes where the transmitter knows the energy arrivals only causally as they happen. We consider the case of general independent and identically distributed (i.i.d.) energy arrivals, and propose a near-optimal strategy coined fractional power constant cut-off (FPCC) policy. We show that the FPCC policy is near-optimal in that it yields rates that are within a constant gap from the optimal rate region, for all system parameters. Next, we study online transmission policies for a two-user multiple access channel where both users harvest energy from nature. The energy harvests are i.i.d. over time, but can be arbitrarily correlated between the two users. The transmitters are equipped with arbitrary but finite-sized batteries. We propose a distributed fractional power (DFP) policy, which users implement distributedly with no knowledge of the other user's energy arrival or battery state. We show that the proposed DFP is near-optimal as in the broadcast channel case. Then, we consider online power scheduling for energy harvesting channels in which the users incur processing cost per unit time that they are on. The presence of processing costs forces the users to operate in a bursty mode. We consider the single-user and two-way channels. For the single-user case, we consider the case of the general i.i.d.~energy arrivals. We propose a near-optimal online policy for this case. We then extend our analysis to the case of two-way energy harvesting channels with processing costs; in this case, the users incur processing costs for being on for transmitting or receiving data. Our proposed policy is distributed, which users can apply independently with no need for cooperation or coordination between them. Next, we consider a single-user channel in which the transmitter is equipped with finite-sized data and energy buffers. The transmitter receives energy and data packets randomly and intermittently over time and stores them in the finite-sized buffers. The arrival amounts are known only causally as they happen. We focus on the special case when the energy and data arrivals are fully-correlated. We propose a structured policy and bound its performance by a multiplicative gap from the optimal. We then show that this policy \emph{is optimal} when the energy arrivals dominate the data arrivals, and is \emph{near-optimal} when the data arrivals dominate the energy arrivals. Then, we consider another performance metric which captures the freshness of data, i.e., AoI. For this metric, we first consider an energy harvesting transmitter sending status updates to a receiver over an erasure channel. The energy arrivals and the channel erasures are i.i.d. and Bernoulli distributed in each slot. In order to combat the effects of the erasures in the channel and the uncertainty in the energy arrivals, we use channel coding to encode the status update symbols. We consider two types of channel coding: maximum distance separable (MDS) codes and rateless erasure codes. For each of these models, we study two achievable schemes: best-effort and save-and-transmit. We analyze the average AoI under each of these policies. We show that rateless coding with save-and-transmit outperforms all other schemes. Next, we consider a scenario where the transmitter harvests i.i.d. Bernoulli energy arrivals and status updates carry information about an independent message. The transmitter encodes this message into the timings of the status updates. The receiver needs to extract this encoded information, as well as update the status of the observed phenomenon. The timings of the status updates, therefore, determine both the AoI and the message rate (rate). We study the trade-off between the achievable message rate and the achievable average AoI. We propose several achievable schemes and compare their rate-AoI performances. Then, with the motivation to understand the effects of temperature sensitivity on wireless data transmission performance for energy harvesting communication networks, we study several temperature models. We assume non-causal knowledge of the energy arrivals. First, we consider throughput maximization in a single-user energy harvesting communication system under continuous time energy and temperature constraints. We model three main temperature related physical defects in wireless sensors mathematically, and investigate their impact on throughput maximization. Specifically, we consider temperature dependent energy leakage, effects of processing circuit power on temperature, and temperature increases due to the energy harvesting process itself. In each case, we determine the optimum power schedule. Next, different from the previous work, we consider a discrete time system where transmission power is kept constant in each slot. We consider two models that capture different effects of temperature. In the first model, the temperature is constrained to be below a critical temperature at all time instants; we coin this model as explicit temperature constrained model. We investigate throughput optimal power allocation for multiple energy arrivals under general, as well as temperature and energy limited regimes. In the second model, we consider the effect of the temperature on the channel quality via its influence on additive noise power; we coin this model as implicit temperature constrained model. In this model, the change in the variance of the additive noise due to previous transmissions is non-negligible. In particular, transmitted signals contribute as interference for all subsequent slots and thus affect the signal to interference plus noise ratio (SINR). In this case, we investigate throughput optimal power allocation under general, as well as low and high SINR regimes. Finally, we consider the case in which implicit and explicit temperature constraints are simultaneously active. Finally, we extend the discrete time explicit temperature constraint model to a multi-user setting. We consider a two-user energy harvesting multiple access channel where the temperatures of the nodes are affected by the electromagnetic waves due to data transmission. We study the optimal power allocations when the temperatures of the nodes are subject to peak temperature constraints, where each node has a different peak temperature requirement and the nodes have different temperature parameters. We study the optimal power allocation in this case and derive sufficient conditions under which the rate region collapses to a single pentagon

In situ high-resolution structure of the baseplate antenna complex in <i>Chlorobaculum tepidum</i>

Photosynthetic antenna systems enable organisms harvesting light and transfer the energy to the photosynthetic reaction centre, where the conversion to chemical energy takes place. One of the most complex antenna systems, the chlorosome, found in the photosynthetic green sulfur bacterium Chlorobaculum (Cba.) tepidum contains a baseplate, which is a scaffolding super-structure, formed by the protein CsmA and bacteriochlorophyll a. Here we present the first high-resolution structure of the CsmA baseplate using intact fully functional, light-harvesting organelles from Cba. tepidum, following a hybrid approach combining five complementary methods: solid-state NMR spectroscopy, cryo-electron microscopy, isotropic and anisotropic circular dichroism and linear dichroism. The structure calculation was facilitated through development of new software, GASyCS for efficient geometry optimization of highly symmetric oligomeric structures. We show that the baseplate is composed of rods of repeated dimers of the strongly amphipathic CsmA with pigments sandwiched within the dimer at the hydrophobic side of the helix

Intermittent Computing: Challenges and Opportunities

The maturation of energy-harvesting technology and ultra-low-power computer systems has led to the advent of intermittently-powered, batteryless devices that operate entirely using energy extracted from their environment. Intermittently operating devices present a rich vein of programming languages research challenges and the purpose of this paper is to illustrate these challenges to the PL research community. To provide depth, this paper includes a survey of the hardware and software design space of intermittent computing platforms. On the foundation of these research challenges and the state of the art in intermittent hardware and software, this paper describes several future PL research directions, emphasizing a connection between intermittence, distributed computing, energy-aware programming and compilation, and approximate computing. We illustrate these connections with a discussion of our ongoing work on programming for intermittence, and on building and simulating intermittent distributed systems