In this work, we propose an adaptive system design for an Internet of Things (IoT) monitoring network with latency and reliability requirements, where IoT devices generate time-critical and event-triggered bursty traffic, and an unmanned aerial vehicle (UAV) aggregates and relays sensed data to the base station. Existing transmission schemes based on the overall average traffic rates over-utilize network resources when traffic is smooth, and suffer from packet collisions when traffic is bursty which occurs in an event of interest. We address such problems by designing an adaptive transmission scheme employing multiuser shared access (MUSA) based grant-free non-orthogonal multiple access and use short packet communication for low latency of the IoT-to-UAV communication. Specifically, to accommodate bursty traffic, we design an analytical framework and formulate an optimization problem to maximize the performance by determining the optimal number of transmission time slots, subject to the stringent reliability and latency constraints. We compare the performance of the proposed scheme with a non-adaptive power-diversity based scheme with a fixed number of time slots. Our results show that the proposed scheme has superior reliability and stability in comparison to the state-of-the-art scheme at moderate to high average traffic rates, while satisfying the stringent latency requirements.

We propose a new spectrum allocation strategy for terahertz (THz) band communication (THzCom) systems. Specifically, we design multi-band-based spectrum allocation with adaptive sub-band bandwidth (ASB), by allowing to divide the spectrum of interest into sub-bands with unequal bandwidths. Due to the frequency and distance-dependent nature of the molecular absorption loss, the variation in this loss between the sub-bands would be very high at the THz band when equal sub-band bandwidth (ESB) is considered, as in the literature. The proposed strategy reduces this variation by allowing changes in the sub-band bandwidth, which leads to an overall improvement in the data rate performance. To study the impact of our strategy, we formulate an optimization problem, with the main focus on spectrum allocation, to determine the optimal sub-band bandwidth and transmit power. Thereafter, we propose reasonable approximations and transformations to solve the formulated problem. Aided by numerical results, we show that by enabling and optimizing ASB, a significantly higher data rate can be achieved by our strategy, compared to adopting ESB, and it is more beneficial to adopt ASB when the spectrum with the highest average molecular absorption loss within the THz transmission window is selected during spectrum allocation.

Federated learning (FL) allows predictive model training on the sensed data in a wireless Internet of Things (IoT) network evading data collection cost in terms of energy, time, and privacy. In this article, for an FL setting, we model the learning gain achieved by an IoT device against its participation cost as its utility. The local model quality and the associated cost differ from device to device due to the device heterogeneity, which could be time varying. We identify that this results in utility unfairness because the same global model is shared among the devices. In the vanilla FL setting, the master is unaware of devices’ local model computation and transmission costs, thus, it is unable to address the utility unfairness problem. In addition, a device may exploit this lack of knowledge at the master to intentionally reduce its expenditure and thereby boost its utility. We propose to control the quality of the global model shared with the devices, in each round, based on their contribution and expenditure. This is achieved by employing differential privacy (DP) to curtail global model divulgence based on the learning contribution. Furthermore, we devise adaptive computation and transmission policies for each device to control its expenditure in order to mitigate utility unfairness. Our results show that the proposed scheme reduces the standard deviation of the energy cost of devices by 99% in comparison to the benchmark scheme, while the standard deviation of the training loss of devices varies around 0.103.

We study spectrum allocation for terahertz (THz) band communication (THzCom) systems, while considering the frequency and distance-dependent nature of THz channels. Different from existing studies, we explore multi-band-based spectrum allocation with adaptive sub-band bandwidth (ASB) by allowing the spectrum of interest to be divided into sub-bands with unequal bandwidths. Also, we investigate the impact of sub-band assignment on multi-connectivity (MC) enabled THzCom systems, where users associate and communicate with multiple access points simultaneously. We formulate resource allocation problems, with the primary focus on spectrum allocation, to determine sub-band assignment, sub-band bandwidth, and optimal transmit power. Thereafter, we propose reasonable approximations and transformations, and develop iterative algorithms based on the successive convex approximation technique to analytically solve the formulated problems. Aided by numerical results, we show that by enabling and optimizing ASB, significantly higher throughput can be achieved as compared to adopting equal sub-band bandwidth, and this throughput gain is most profound when the power budget constraint is more stringent. We also show that our sub-band assignment strategy in MC-enabled THzCom systems outperforms the state-of-the-art sub-band assignment strategies and the performance gain is most profound when the spectrum with the lowest average molecular absorption coefficient is selected during spectrum allocation.

We consider uplink machine-type communication (MTC) from energy-constrained devices following the time division multiple access (TDMA) protocol. Conventionally, the energy efficiency performance in TDMA is optimized through multi-user scheduling, i.e., changing the transmission block length allocated to different devices. In such a system, the sequence of devices for transmission, i.e., who transmits first and who transmits second, etc., has not been considered as it does not have any impact on the energy efficiency. In this work, we consider that data compression is performed before transmission and show that the multi-user sequencing is indeed important. We propose to jointly optimize both multi-user sequencing and scheduling along with the compression and transmission rate control. Our results show that multi-user sequence optimization significantly improves the energy efficiency performance of the system, and especially the performance gain is large when the delay bound is stringent. This is advantageous for lower latency MTC.

In this paper, we propose joint sequencing and scheduling optimization for uplink machine-type communication (MTC). We consider multiple energy-constrained MTC devices that transmit data to a base station following the time division multiple access (TDMA) protocol. Conventionally, the energy efficiency performance in TDMA is optimized through multi-user scheduling, i.e., changing the transmission block length allocated to different devices. In such a system, the sequence of devices for transmission, i.e., who transmits first and who transmits second, etc., has not been considered as it does not have any impact on the energy efficiency. In this work, we consider that data compression is performed before transmission and show that the multi-user sequencing is indeed important. We apply three popular energy-minimization system objectives, which differ in terms of the overall system performance and fairness among the devices. We jointly optimize both multi-user sequencing and scheduling along with the compression and transmission rate control. Our results show that multi-user sequence optimization significantly improves the energy efficiency performance of the system. Notably, it makes the TDMA-based multi-user transmissions more likely to be feasible in the lower latency regime, and the performance gain is larger when the delay bound is stringent.

We consider a machine-type communication (MTC) node that is served by a hybrid access point (HAP) which provides RF power transfer to the node and receives data transmission from the node. Due to the lossy wireless medium and limited efficiency of RF energy transducer, the energy cost at the HAP is substantial. To minimize the energy cost while still satisfying the system requirement, the harvested energy at the MTC node must be used efficiently. To this end, we consider that the MTC node employs data compression in order to reduce the energy cost of data transmission. Data compression itself consumes time and energy, which needs to be carefully controlled. Thus, we propose to jointly optimize the harvesting-time, compression and transmission design, to minimize the energy cost of the system under given delay constraint. The proposed scheme achieves up to 19% performance gain, under given system constraints, as compared to optimizing harvesting-time ratio and transmission rate without employing compression.

For a sensor based machine-type communication (MTC) device, transmission is a power hungry operation and blindly applying too much data compression may even exceed the cost of transmitting raw data, thus losing its purpose. Hence, it is important to investigate the trade-off between data compression and transmission energy costs. We consider a system that is composed of an energy constrained sensor based MTC device and a sink node, and devise an optimal data compression and transmission policy with an objective to maximize the lifetime of the sensor based MTC device whilst satisfying specific delay and bit error rate (BER) constraints when statistical channel gain is known at the sensor node. Our results show that a jointly optimized compression-transmission policy achieves 100% to 1500% better performance as compared to optimizing transmission only without compression under given BER and delay constraints. Importantly, the gain is most profound in the low latency regime.

We consider a system that is composed of an energy constrained sensor node and a sink node, and devise optimal data compression and transmission policies with an objective to prolong the lifetime of the sensor node. While applying compression before transmission reduces the energy consumption of transmitting the sensed data, blindly applying too much compression may even exceed the cost of transmitting raw data, thereby losing its purpose. Hence, it is important to investigate the trade-off between data compression and transmission energy costs. In this paper, we study the joint optimal compression-transmission design in three scenarios which differ in terms of the available channel information at the sensor node, and cover a wide range of practical situations. We formulate and solve joint optimization problems aiming to maximize the lifetime of the sensor node whilst satisfying specific delay and bit error rate constraints. Our results show that a jointly optimized compression-transmission policy achieves significantly longer lifetime (90% to 2000%) as compared to optimizing transmission only without compression. Importantly, this performance advantage is most profound when the delay constraint is stringent, which demonstrates its suitability for low latency communication in future wireless networks.

Devices in Low Power and Lossy Networks (LLNs) based Internet of Things (IoT) systems have energy, memory and processing constraints. For LLNs, IETF standardized a light-weight routing protocol referred to as RPL. The energy efficiency of RPL routing protocol has been under investigation in prior studies but its stability issue is recently identified. The stability of RPL is critical for its energy efficient and application oriented routing operation. In RPL, child nodes select best parent towards the sink node based on some Objective Function (OF). Current OFs proposed for RPL result in unstable operation of RPL due to frequent route changes. Consequently, high control traffic is generated to re-construct routes which consumes scarce energy resources and thus reduces the network lifetime. In this paper, a novel OF for RPL is proposed which exploits channel adaptability and stability provided by ETX and HOP routing OFs, respectively. The proposed enhancement of RPL is implemented in Contiki operating system and its analysis is carried out in Cooja simulator. The analysis suggests reduction in frequent parent changes, control traffic and energy consumption, thereby improves RPL stability and energy efficiency.

Development of Low-power Low-rate wireless Networks (LLNs) based systems is prodigiously escalated, which helps realizing the notion of Internet of Things (IoT). Routing Protocol for Low-power and Lossy Networks (RPL) is the standard routing protocol for IPv6 LLNs. RPL has gotten tremendous appreciation due to its flexibility and adaptiveness to support dynamic application requirements in IoT. In this paper, we identify multiple issues in RPL route maintenance and recovery mechanism and investigate their drastic effects on the performance of RPL based on a simulation as well as an experimental study using Zolertia Z1 motes. In addition, the paper proposes an enhanced route maintenance and recovery mechanism to improve and rectify RPL operation. Performance analysis of the proposed scheme promise significantly lower reconnection time and high certainty in route recovery.

In several use-cases of Internet of Things (IoT), IEEE 802.11 based WLANs are more favorable due to superior data rate support even though their energy efficiency is not up to the mark. Particularly, wireless multimedia sensors based WLANs demand higher energy resources. In this regard, various IEEE 802.11 based power saving mechanisms are developed. IEEE 802.11n standard specifies Power Save Multiple Poll (PSMP) protocol. However, PSMP is infeasible for many IoT based systems specifically in use-cases where multi-hop communication is required. Moreover, PSMP scheduling mechanism lacks the capability to adapt to the dynamic Quality of Service (QoS) requirements in Internet of Multimedia Things (IoMT). In this paper, a QoS aware Multi-Hop PSMP (mPSMP) protocol is proposed to enable energy efficient multimedia communication over IoT. The mPSMP incorporate a traffic scheduling model to allocate channel resources in a time division multiple access manner. Therein, adaptive duty cycling is employed to minimize energy utilization, while assuring the required multimedia QoS for each node. The proposed protocol is implemented in Network Simulator-2 (NS-2). Analytical analysis and simulation study suggests reduction in end-to-end delay and duty cycling along with significant improvement in energy efficiency of IoMT devices.

In low-power low-rate wireless networks, IEEE 802.15.4 is a standard protocol for communication. Devices in such networks are usually battery-operated so radio-transceiver component in such networks are typically of low range but most power consuming. Proper antenna orientation, distance between nodes and channel selection are among the ways to achieve reliability in these networks. But these arrangements can affect the link qualities and achieved communication performance in IoT applications. In this work, we have evaluated the performance of IEEE 802.15.4 links using Zolertia Z1 motes experimentally through indoor/outdoor real scenarios. We have found that RSSI decreases with distance and is effected by the height of the motes. The antenna polarization drastically affects the RSSI whereas LQI and packet delivery ratio is not much affected. We also found that the non-interfering channels 26 and channel 15 are effected by Wi-Fi in the same way. Moreover the contiki MAC performs better than XMAC protocol.

Recent advancements in PHY data rates of communication technologies require efficient MAC mechanisms. IEEE 802.11 MAC compels nodes to defer transmissions to avoid collisions and maintain random and fair channel access. Contention time degrades network performance in terms of bandwidth and delays; this problem exaggerates at higher data rates. In this paper, we propose a frequency-domain backoff scheme, where each OFDM data subcarrier is assigned an integer and then RTS is transmitted using a randomly chosen data subcarrier. In parallel using an additional listening antenna, each node listens on whole band for all active subcarriers. Node that transmits on smallest subcarrier wins channel access and is replied with CTS. Unlike IEEE 802.11 backoff, this handshaking frequency-domain backoff mechanism has lesser and fixed contention overhead. We also compute goodput efficiency of proposed mechanism using an analytical model. Our analysis suggests that proposed mechanism has higher efficiency gains in various network scenarios.