This article considers an operator that deploys unmanned aerial vehicles (UAVs) to service requests from users/devices in an Internet of Things (IoT) network; each request requires one or more so-called services, such as a database or an Artificial Intelligence (AI) model. A key problem faced by the operator is to determine which services to place on UAVs and which requests to transport to a base location for execution; note, UAVs have resource constraints and hence, they are unable to carry all services or/and serve all users in one trajectory. Further, they do not have connectivity to a base station during flight. To this end, given a set of requests, this article presents novel solutions that minimize the total completion time of these requests. First, it outlines a novel mixed-integer linear program (MILP) that can be used to calculate the optimal assignment of services to trajectories. MILP also decides which requests are transported to a base location for computation, and schedules the return of result(s) to users. The second solution is a heuristic method named MinTime that chooses UAVs and users according to traveling and task execution time. The results show MinTime achieved 91.15% of the optimal result in terms of request completion time.

In future Internet of Thing (IoT) networks, devices can be leveraged to compute tasks or services. To this end, this article addresses a novel problem that requires devices to collaboratively execute tasks with dependencies. A key consideration is that in order to conserve energy, devices may execute a task in approximate mode, which generate errors. To optimize their operation mode, we outline a novel chance-constrained program that aims to execute as many tasks as possible in approximate mode subject to a probabilistic constraint relating to the said errors. We also outline two novel solutions to determine task execution modes: 1) a sample average approximation (SAA) method and 2) a heuristic solution called minimum communication cost (MinC). We have studied the performance of SAA and MinC with round robin (RR), which assigns tasks to devices in an RR manner. Specifically, we find that the maximum energy consumption of devices when using MinC and RR is, respectively, around 14.2% and 23.1% higher than SAA, which yields the optimal solution. Further, MinC results in approximately 27.9% lower energy consumption as compared to RR.

In Internet of Things (IoT) networks, Unmanned Aerial Vehicles (UAVs) play a critical role as mobile nodes that can be deployed to carry out data collection and computation. In this respect, this paper considers an operator that deploys UAVs to satisfy requests from IoT applications that require Virtual Network Functions (VNFs) that may communicate with one another to be executed at different geographical locations. To this end, this paper formulates a novel Mixed Integer Linear Program (MILP) to determine the optimal assignments of UAVs and VNFs over a planning horizon that maximizes a given performance metric, e.g., revenue. It also outlines a heuristic method named MPopLoc that chooses requests according to popular requested locations and traveling cost of UAVs. The results show that MPopLoc achieved approximately 95.14% of the optimal result.

This paper considers targets coverage in energy harvesting Internet of Things (IoT) networks. Specifically, solar-powered sensor devices employ network virtualization technology to partition their resources, such as energy, memory, and computation workload, in order to serve requests with different coverage requirements. Our objective is to maximize the revenue from completing requests. To this end, we outline a mixed integer linear program (MILP) to optimize the start time of each request and the set of nodes that serve a request. We also propose a heuristic, called energy harvesting aware request placement (EHARP), to determine requests to be deployed in each time slot based on energy harvesting conditions and the resource state of sensor nodes. Furthermore, we propose two model predictive control (MPC) approaches, called MPC-MILP and MPC-EHARP, respectively, which deploy requests based on energy arrival at devices over a given time window as predicted by a Gaussian mixture model (GMM). Simulation results show that EHARP, MPC-MILP, and MPC-EHARP are 94.75%, 88.73%, and 87.6% optimal. In addition, the revenue obtained by EHARP is 173.8% higher than a competing approach.

This article studies a coverage problem in Internet of things (IoT) networks using unmanned aerial vehicles (UAVs) supported by solar-powered charging platforms. The problem at hand is to determine an assignment of UAVs to either a charging station or a monitoring point over a planning horizon. A key constraint is$K$-coverage, where given a set of$\mathcal {M}$points,$K$of these points must be monitored by a UAV in each time slot. In this respect, the paper aims to design UAVs assignment solutions that yield the longest$K$-coverage lifetime. We formulate a novel mixed integer linear program (MILP) to jointly optimize UAVs assignments over a given planning horizon. The problem is challenging as the energy level of charging platforms and UAVs are coupled across time slots. Moreover, the formulated MILP requires non-causal energy arrivals information at charging platforms. To this end, we outline a model predictive control (MPC) and a Monte Carlo tree search (MCTS) based solution that use non-causal energy arrivals information. The simulation results show that MPC and MCTS achieve approximately 81.04% and 67.07% of the optimal results computed by MILP.

This article considers task scheduling in solar-powered Internet of Things (IoT) networks where devices are capable of sharing energy wirelessly. Our aim is to minimize the completion time of all tasks. We outline a novel mixed-integer linear program (MILP) to schedule tasks and determine whether devices share their harvested energy via radio frequency (RF) in each time slot. The MILP considers the coupling between the energy level at devices across time slots. It also considers the dependency of tasks, whereby each task must be executed on a given set of devices in a specific order. Further, we propose a heuristic algorithm called minimum time first with energy sharing (MinTime-ES) for large scale networks. Our results show that with energy sharing, MILP and MinTime-ES achieve 28.86% and 7.83% reduction in task completion time as compared to competing algorithms that do not consider energy sharing between devices.

This article studies a novel wireless-powered Internet of Things (IoT) network that consists of: 1) a hybrid access point (HAP) that charges devices and also helps facilitate backscattering transmissions; 2) devices that use active radio frequency (RF) and backscattering transmissions; and 3) a mobile data collector. Our aim is to maximize the amount of data received by the HAP and data collector. The main problem is to determine the charging duration of the HAP and link activation schedule of devices. We formulate a novel mixed-integer linear program (MILP) and also propose a heuristic algorithm named reduced-set linear program approximation (RS-LPA). The results show that: 1) throughput increases with the number of backscatter transmission sets; 2) smaller amount of data is uploaded to the mobile collector when sampling cost is low; and 3) the throughput of RS-LPA is on average 10.55% lower than MILP.

The modeling and measurement of the biodynamic response of the seated human body has recently been an active research topic, with major applications to ergonomics and automotive suspension control system technologies. This paper presents a holistic literature survey of topics including the latest research in the area of vibration signal processing and modeling of the biodynamic response of the seated human to vibrations. This paper reviews recent sensing systems that are reported to measure the motion of the seated body. The data processing techniques that are currently accepted are surveyed and these include impedance, transmissibility measures, frequency response function estimation, and model development. A review of applications of biodynamic response analysis and modeling to seating vibration isolation technologies and vibration monitoring systems is presented within this paper. This survey paper provides a discussion on the direction that the future research in this field will aim toward based on the trends in the recent research and the introduction and application of new technologies.

A method of approximating the Rician K-Factor with considerations of the local human-built environment is proposed for urban VANETs. The model is validated experimentally on a busy street in Australia, in the presence and absence of other vehicles. The model is found to accurately predict actual channel measurements in close-range communications scenarios.

Vehicle side slip angle is a critical variable used in car safety systems like Electronic Stability Control. Due to the practical difficulty in direct measurement of side slip angle, accurate estimation of vehicle side slip angle using available signals is becoming important. This paper presents a novel algorithm for estimating the side slip angle of a vehicle in real time using inertial motion sensors. The algorithm uses a J48 decision tree classifier to assist the Extended Kaiman Filter (EKF) predictions of the vehicle side slip angle. The decision tree classifies the inertial data into classes based on the condition the slip angle is expected to be in. Using the class information asserted by the classifier, the error covariance parameter of the EKF is adjusted to compensate for changes in disturbances and nonlinearities. The results show that the decision tree assisted EKF technique presented in this paper is capable of predicting the slip angle with sound accuracy using inertial motion data.

This is a simulation study of weight-based and precedence-based clustering paradigms in VANETs under an experimentally-derived channel model. The study reveals that CH election schemes accounting for driver intention form more stable and long-lived clusters. The results also show that ignoring elements of the VANET channel (such as vehicular shadowing) results in an unreliable comparative analysis of protocol performance.

CORNER is an urban propagation model which simulates the presence of buildings in city scenarios and models radio propagation as a series of reflections and diffractions around buildings. CORNER was validated in the original publication with a series of packet-delivery-ratio measurements. However, the accuracy of these measurements is limited by interference from nearby networks. This paper independently evaluates the CORNER model using signal strength measurements across three separate sites in Sydney and Wollongong. The measurements are analysed and compared with the predicted analytical estimates. The fading model is also analysed with direct measurements. A new CORNER link classification algorithm is also proposed in this paper.

The wireless sensor node architecture proposed in this paper is optimized for use in a wireless interactive point, listen and see system. In particular, we focus on developing a wireless sensor node that can be remotely charged by harvesting microwave energy. The current system implementation allows a user to access information from a remote sensor via their mobile computing device. These sensors are limited in complexity due to the limited power available, and are cumbersome since manual intervention is required to replace its batteries. We propose a system where battery powered wireless sensor nodes can be recharged by harvesting energy from a microwave Radio Frequency (RF) signal source. The remote power charging module of the wireless sensor node architecture consisted of an antenna array and a rectification circuit. A prototype of the antenna array and rectification circuit of the remote power charging module for the wireless sensor node was constructed and is presented in this paper.

The emergence of telemedicine developments and the implementation of wireless networks in the medical field have paved the way for research into how we can best harness wireless technology to provide the medical sector with robust, accurate and 'usable' solutions. This paper describes the implementation of a medical information system network that allows medical instrumentation data to be accessed and controlled by handheld devices operated by medical practitioners. Embedded platforms are used for the medical information nodes that communicate with ECG instruments. We explore and compare the performance of implementing real-time acquisition of patient ECG signals on an FPGA with a software implementation. It was found that the ECG module implemented as custom logic occupied a smaller percentage of the CPU time and also consumed less current than the software implementation

Code compression is a field where compression ratios between compiler-generated code and subsequent compressed code are highly dependent on decisions made at compile time. Most optimizations employed by compilers tend to focus on parameters such as program performance, minimizing resource dependencies and sometimes the option of reducing code size.This paper describes a post-compilation technique for the greedy reassignment of general purpose scratch registers to improve Hamming distance based code compression. The code translation renumbers registers based on the frequency of registers used by isomorphic instructions and employs a Gray coding scheme to reduce Hamming distances between similar instructions.Register reassignment has been successfully implemented in areas where the compiler optimizations do not include a particular metric, for example, power savings. Pro-gram values can be reassigned register numbers that reduce overall power consumption of the address bus and register file decoder, at no cost to code size or performance.The application of the register reassignment technique in this paper reduced the number of dictionary vectors required by a program on average by 9.74%. Code compression ratios of register-reassigned binaries were consistently around 3-4% (of original program size) lower than code compression applied to original binaries, with the highest such reduction at nearly 7%. General purpose register reassignment is a technique that allows for gains in compression efficiency with no penalty in hardware. Other techniques that could be trialed include commutative register switching, dead register detection and assignment and complete register re-allocation.

This paper presents and reports on a VLIW code compression technique based on vector Hamming distances. It investigates the appropriate selection of dictionary vectors such that all program vectors are at most a specified maximum Hamming distance from a dictionary vector. Bit toggling information is used to restore the original vector.A dictionary vector selection method which considered both vector frequency as well as maximum coverage achieved better results than just considering vector frequency or vector coverage independently. This method was found to outperform standard dictionary compression on TI TMS320C6x program code by an average of 8%, giving compression ratios of 72.1% to 80.3% when applied to the smallest compiler builds. The most favorable results were achieved with a Hamming distance upper limit of 3.An investigation into parallel compression showed that dividing the program into 32-bit parallel streams returned an average compression ratio of 79.4% for files larger than 200kb. This approach enables parallel decompression of instruction streams within a VLIW instruction word. Suggestions for further work include compiler/compression integration, more sophisticated dictionary selection methods and better codeword allocation.

Code compression algorithms usually require specific techniques to maintain the integrity of the program and ensure its functionality. This paper presents three code compression algorithms: the first encoding scheme used is a dictionary compression method that compresses the original program by replacing every instruction with its corresponding reference into the dictionary. The second compression algorithm exploits the facts that 1) the entire op-code space is usually not used and 2) operand patterns are often common between different instructions. The third compression scheme groups instructions into 8-instruction words and the corresponding instruction/ operand bits from all 8 instructions are grouped together to form dictionary entries. Instruction Factorization was found to be the most efficient compression scheme, though decompression is done sequentially. Although this technique may be very advantageous to single-issue processors. Operand Factorization across instruction-words allows decompression to be parallelized for instructions in the same instruction word, however this is at a cost to compression ratio.

Code size has always been an important issue for all embedded applications as well as larger systems. Code compression techniques have been devised as a way of battling bloated code; however, the impact of VLIW compiler methods and outputs on these compression schemes has not been thoroughly investigated.This paper describes the application of single- and multiple-instruction dictionary methods for code compression to decrease overall code size for the TI TMS320C6xxx DSP family. The compression scheme is applied to benchmarks taken from the Mediabench benchmark suite built with differing compiler optimization parameters.In the single instruction encoding scheme, it was found that compression ratios were not a useful indicator of the best overall code size - the best results (smallest overall code size) were obtained when the compression scheme was applied to size-optimized code. In the multiple instruction encoding scheme, changing parallel instruction order was found to only slightly improve compression in unoptimized code and does not affect the code compression when it is applied to builds already optimized for size.