It was recently shown that the introduction of video art in STEAM (Science, Technology, Engineering, Art, Math) education alleviates negative academic emotion and also creates a positive learning experience for university science students. Furthermore, students' writing apprehension levels towards technical writing may be manipulated via pre-writing techniques and strategies, such as free-writing. Notably, it has been maintained that the English for Academic/English for Scientific Purposes (EAP/EScP) courses can provide fruitful grounds for the investigation of the class-related state emotion. In the present study, we devised a pre-technical writing teaching intervention that involved a STREAM (Science, Technology, Reading/Writing, Engineering, Art, Math) lesson plan. This comprised of William Blake's poetry presented in the context of his illuminated books of Songs of Innocence and Experience, with an emphasis on the analysis of his poem, the Tyger. Subsequently, students engaged with an Augmented Reality (AR) art book and created their own AR artwork. Students were requested to record their emotions and attitudes on the art and technology involved. Also, they were asked to produce a written piece on their views on the visual languages and realities that they experienced. The assessment of academic emotion occurred prior-and post-teaching, followed by the experience evaluation of the intervention. Results denote a positive experience, a partial increase in positive emotion and a generalized drop of intense negative academic emotion, and are thus in line with recent studies in the field.

Supporting effective load balancing is paramount for increasing network utilization efficiency and improving the perceivable user experience in emerging and future cellular networks. At the same time, it is becoming increasingly alarming that current communication practices lead to excessive energy wastes both at the infrastructure side and at the terminals. To address both these issues, this paper discusses an innovative communication approach enabled by the implementation of device-to-device (d2d) communication over cellular networks. The technique capitalizes on the delay tolerance of a significant portion of Internet applications and the inherent mobility of the nodes to achieve significant performance gains. For delay-tolerant messages, a mobile node can postpone message transmission-in a store-carry and forward manner-for a later time to allow the terminal to achieve communication over a shorter range or to postpone communication to when the terminal enters a cooler cell, before engaging in communication. Based on this framework, a theoretical model is introduced to study the generalized multihop d2d forwarding scheme where mobile nodes are allowed to buffer messages and carry them while in transit. Thus, a multiobjective optimization problem is introduced where both the communication cost and the varying load levels of multiple cells are to be minimized. We show that the mathematical programming model that arises can be efficiently solved in time. Furthermore, extensive numerical investigations reveal that the proposed scheme is an effective approach for both energy-efficient communication and offering significant gains in terms of load balancing in multicell topologies.

The optimal power allocation for block fading (BF) networks with confidential messages is investigated under anM-block delay and power constraint. First, we study networks without channel state information (CSI) feedback to the transmitter and demonstrate that the optimal power allocation is the equidistribution of the power budget, denoted as the “blind policy.” In blind scenarios secrecy can be achieved through receiver diversity; the probability of secrecy outage (PSO) is shown to decay exponentially with the diversity order of the legitimate user. Then, we investigate networks with CSI feedback. For comparison purposes, we restate the acausal secure waterfilling algorithm with full CSI before moving to the causal feedback scenario. In the latter, an approximate “threshold policy” for the low SNR and an approximate “high power policy” for the high SNR regimes are derived. Furthermore, a novel universal transmission policy is proposed across all SNRs, denoted as the “blind horizon approximation” (BHA). Through numerical results, the BHA policy is shown to outperform both the threshold and high power policies when the legitimate user has an SNR advantage with respect to the eavesdropper, while it also compares well with the secure waterfilling policy.

The optimal power allocation that maximizes the secrecy capacity (SC) of block fading Gaussian (BF-Gaussian) networks with causal channel state information (CSI), M-block delay tolerance and a frame based power constraint is examined. In particular, the SC maximization is formulated as a dynamic program. First, the SC maximization without any information on the CSI is studied; in this case the SC is maximized by equidistribution of the power budget, denoted as the "blind policy". Next, extending earlier results on the capacity maximization of BF-Gaussian channels without secrecy constraints, transmission policies for the low SNR and the high SNR regimes are proposed. When the available power resources are very low the optimal strategy is a "threshold policy". On the other hand when the available power budget is very large a "constant power policy" maximizes the frame secrecy capacity. Subsequently, a novel universal transmission policy is introduced, denoted in the following as the "blind horizon approximation" (BHA), by imposing a blind policy in the horizon of unknown events. Through numerical results, the novel BHA policy is shown to outperform both the threshold and constant power policies as long as the mean channel gain of the legitimate user is distinctively greater than the mean channel gain of the eavesdropper. Furthermore, the secrecy rates achieved by the BHA compare well with the secrecy rates of the secure waterfilling policy in the case of acausal CSI feedback to the transmitter.

This work is concerned with the problem of efficient and intelligent message forwarding in wireless networks. This problem arises in many diverse scenarios within ad-hoc networks and especially networks formed during and in the aftermath of a disaster in which infrastructure-based communication systems have been damaged or completely destroyed. Within this setting, mobile devices need to support critical message exchanges in order to offer user reassurance and aid first responders' search-and-rescue operations. Notably, the dissemination of alert messages has to be done in a way that achieves sufficient dissemination while ensuring network longevity. Under the proposed explore and exploit (EnE) framework, this paper derives innovative networking heuristics that capitalizes on locally-calculated metrics (including the Local Connectivity (LC) centrality metric) to make message forwarding/replication decisions. The proposed heuristics exhibit excellent features with regards to the aforementioned performance objectives and are shown to greatly outperform current popular alternative solutions.

In this paper, store-carry and forward (SCF) decision policies for relaying within the cell are developed. The key motivation of SCF relaying stems from the fact that energy consumption levels can be dramatically reduced by capitalizing on the inherent mobility of nodes and the elasticity of Internet applications. More specifically, we show how the actual mobility of relay nodes can be incorporated as an additional resource in the system to achieve savings in the required communication energy levels. To this end, we provide a mathematical programming formulation on the aforementioned problem and find optimal routing and scheduling policies to achieve maximum energy savings. By investigating structural properties of the proposed mathematical program we show that optimal solutions can be computed efficiently in time. The tradeoffs between energy and delay in the system are meticulously studied and Pareto efficient curves are derived. Numerical investigations show that the achievable energy gains by judiciously storing and carrying information from mobile relays can grow well above 70 percent for the macrocell scenario when compared to a baseline multihop wireless relaying scheme that uses shortest path routes to the base station.

Cellular networks are currently facing significant challenges as mobile Internet access adoption continues to grow over the subscriber base. The challenge that network operators are facing is that Internet data traffic consumes considerably more resources than voice calls, necessitating in that respect significant capacity enhancements. Further, this rise in system utilization has caused a considerable increase in the energy consumption expenditure of both the access network components and the user terminals; in the former case causing a significant increase in cost, for the second case a reduction in the usability of battery operated terminals and for both cases a peak in the carbon footprint of telecoms equipment. Significant research effort has been placed recently in finding innovative solutions to support this boost in data usage demand. In this paper we detail a message forwarding strategy for cellular networks whereby capitalizing on the inherent delay tolerance of Internet type services and utilizing the mobility of nodes, intelligent information forwarding decisions can be made to achieve substantial reductions in the communication energy consumption. We devise optimal look-ahead strategies in which information on either the uplink/downlink is communicated only at the best locations within the system's coverage area to achieve a required performance target. Both analytical and experimental results are presented, showing that significant reductions on the aggregate energy consumption levels can be achieved by the proposed technique.

With the tremendous increase in mobile data traffic, system capacity considerations are no longer the primary and only concern to optimize for in cellular networks. The step increase in utilization of cellular networks not only has shorten the recharging cycle of mobile terminals but has further caused a considerable rise in the operators' energy bill. It has therefore become imperative for the sustainable proliferation of such systems to reduce the energy waste and maintain low operation energy cost. As we discuss in the sequel, mechanical relaying is purposefully envisioned to achieve the required performance gains that need to be realized in order to keep up with the exponential increase in data traffic demand. Via mechanical relaying, mobile nodes are able to postpone message communication while in transit and initiate communication only when found at locations within the cell with favorable channel gains. We show that such a scheme offers the possibility to realize innovating relaying strategies that reduce many-fold the system energy consumption and increase the resource utilization efficiency. In this work, both centralized and decentralized solutions that employ mechanical relaying are considered.

We bring to the fore a novel load balancing technique based on delay tolerant message forwarding that relies on the store-carry and forward paradigm by utilizing the mobility of vehicular nodes in multi-cell wireless networks. Considerations are made not only on the achievable load balancing performance but also on the en route energy consumption. That accounts for the optimal trade-offs between load balancing, communication energy consumption and message delivery delay in the cellular network. A mathematical program is formulated for finding optimal forwarding decision policies for the proposed store-carry and forward (SCF) relaying scheme for load balancing. Furthermore, a low complexity on-line algorithm for the proposed network setup is derived and its performance is compared with the optimal solution. To sharpen the understanding of the proposed message forwarding techniques, a wide set of numerical investigations are presented revealing that by trading-off message delivery delays, the variance of the load across cells can be dramatically reduced.

The integration of mobile relays in cellular networks has been recently envisioned as a way of increasing system capacity, coverage and reducing communication energy consumption. In we studied how store carry and forward (SCF) relaying within the cell can be utilized as an underlay message forwarding mechanism to achieve system wide energy savings. In this paper, by deriving optimal routing policies to reduce either the total energy consumption or the total transmit power, we show that the SCF relaying scheme reduces considerably inter-cell interference. We detail the factors affecting the maximum interference reductions, including the delay tolerance of elastic data traffic and flow characteristics of vehicles in the road network which in this case are used as mobile relay nodes.

In this paper we address the issue of store-carry and forward (SCF) relaying within a cell in a mobile network. The proposed scheme can be considered as a generalization of various multihop wireless relaying schemes, where storing and carrying an information message by a mobile relay node is not allowed. The key motivation of utilizing SCF relaying within the cell is that energy consumption levels can be dramatically reduced by capitalizing on the inherent mobility of nodes and the elasticity of Internet applications. In that respect, we consider a novel multihop cellular architecture to achieve energy savings both at the network side (i.e., Base Stations) and at the user terminals. The proposed scheme makes use of mobility information of relays (vehicles) while roaming inside the cell to device flows that could achieve maximum energy savings. We show that under SCF relaying large energy savings can be achieved by tolerating a controlled delay over the initiated service.

A family of mathematical programs for both the un- capacitated and capacitated joint gateway selection and routing (U/C-GSR) problem in wireless mesh networks are presented. We detail a reformulation using the shortest path cost matrix (SPM) and prove that it gives the optimal solution when applied to the uncapacitated case but can lead to an arbitrary large optimality gap in the capacitated case. Furthermore, an augmented mathematical program is developed where link capacities are allowed to take values from a discrete set depending on the link distance. In this case, the multi-rate capabilities of WMNs (via, for example, adaptive modulation and coding) can be modeled. Evidence from numerical investigations shows that using the SPM formulation realistic network sizes of WMNs can be solved.

We propose a framework that produces robust schedules in collision-free medium access schemes. We demonstrate the approach on the STDMA link scheduling problem that seeks to minimize the frame length using the physical interference model and stochastic link gains. By using conservative link gain values as opposed to average values in the SINR-target constraints, we show that the proposed approach produces shorter schedules when timeslots required for retransmission are taken into account. We derive properties on the expected frame length and provide bounds on the probability of SINR constraint violation and on the number of timeslots needed for retransmission.

The problem of multi-rate power-controlled collision-free scheduling in spatial time division multiple access (STDMA) wireless mesh networks is formulated as a mathematical program utilising cross layer information. As these mixed integer linear programs are intractable (${\cal NP}$-hard problems), optimal collision-free schedules can be found only for topologies consisting of a few nodes. To this end, approximation algorithms that are based on linear programming relaxation and randomised rounding are studied. The proposed framework, which aims to maximise the spatial timeslot reuse under predefined signal-to-interference noise ratio thresholds, is suitable for providing centralised scheduling in the mesh mode of the IEEE 802.16 standard. Performance aspects of the approximation algorithms under different scenarios are investigated.

In this paper, a family of integer linear programs is formulated for performing collision free scheduling in Spatial-TDMA wireless mesh networks. We extend previous formulations for power aware STDMA scheduling to include discrete power transmission and multi-rate support via adaptive constellation selection. Despite the theoretical attractiveness of these (mixed) integer linear programs, STDMA scheduling problems are in general intractable (NP-hard problems). Thus, the practicality of providing optimal solutions is rather limited. To this end, we study approximation algorithms that are based on linear programming relaxation and randomized rounding. Based on these approximation algorithms we focus our study on the trade-off between optimality of the solution and feasibility. Conducted numerical investigations aim to vindicate the claim regarding the strength of randomized algorithms. In that respect the performance of the approximation algorithms under different scenarios, such as the number of active links and number of nodes in the mesh network, is investigated.

In this paper a power and rate control scheme for downlink packet transmission in CDMA networks is proposed. Under the assumption of stochastic packet arrivals and channel states the base station transmits to multiple mobile user at any time instant within rate and power capacity constraints. The objective is to maximize system throughput, while taking into account the queue length distribution over a time horizon. We are interested in optimal rate allocation policies over time and thus we formulate the problem as a discrete stochastic dynamic program. This dynamic program (DP) is exponentially complex in the number of users which renders it impractical and therefore we use an approximate dynamic programming algorithm to obtain in real time sub-optimal rate allocation policies. Numerical results reveal that the proposed algorithm increased the performance (in terms of a number of different measured parameters such as average queue size) of at least 3.5 times compared to a number of different baseline greedy heuristics