Neighboring Devices Research Articles

Role of oxide charges on the voltage and current coupling effects between adjacent devices examined by concentric metal-insulator-semiconductor (MIS) tunnel diodes with ultra-thin oxide

The impacts of positive oxide charges within the silicon oxide (SiO2) layer on the coupling mechanism in metal-insulator-semiconductor (MIS)-coupled devices were studied. By employing the post-metallization annealing (PMA) process, the condition of coupled devices with fewer oxide charges was established. In terms of coupled voltage, the presence of oxide charges facilitated the transmission of gate voltage-induced quasi-Fermi level splitting to neighboring devices, resulting in the induction of coupled voltage. Conversely, with few oxide charges, no coupled voltage was observed at the neighboring device. Additionally, positive oxide charges induced an inversion channel at the silicon surface, fostering the sharing of minority carriers in the conducting channel between adjacent devices. The saturation currents of non-PMA devices were 104 times larger than PMA devices. Less oxide charge will induce less coupling. These mechanisms were validated through TCAD simulations. Furthermore, the impact of oxide charges on photo-sensing was discussed, revealing that oxide charges enhanced the light absorption area and increased photon-induced carriers. However, the PMA devices showed a larger light-to-dark current ratio (>300) due to a smaller dark current.

A secure edge computing model using machine learning and IDS to detect and isolate intruders

The article presents a secure edge computing model that utilizes machine learning for intrusion detection and isolation. It addresses the security challenges arising from the rapid expansion of IoT and edge computing. The proposed Intrusion Detection System (IDS) combines Linear Discriminant Analysis (LDA) and Logistic Regression (LR) to swiftly and accurately identify intrusions without alerting neighboring devices. The model outperforms existing solutions with an accuracy of 96.56%, precision of 95.78%, and quick training time (0.04 s). It is effective against various types of attacks, enhancing the security of edge networks for IoT applications.•The methodology employs a hybrid model that combines LDA and LR for intrusion detection.•Machine learning techniques are used to analyze and identify intrusive activities during data acquisition by edge nodes.•The methodology includes a mechanism to isolate suspected devices and data without notifying neighboring edge nodes to prevent intruders from gaining control over the edge network.

Computational intelligence-enabled prediction and communication mechanism for IoT-based autonomous systems

Autonomous systems and the Internet of Things (IoT) have become more sophisticated research areas and entered successfully into various daily living activities such as smart homes & buildings, autonomous cars, drones, robots, etc. A crucial and essential aspect of these systems is the precision and accuracy of the decision-making process, i.e., the decision support system. Likewise, developing a completely autonomous system is an open research problem. This paper proposes a computational intelligence-based prediction and communication mechanism for the independent system where IoT is used as a data collection tool. Initially, energy gauge (EG) devices collect helpful information about neighboring devices in the IoT networks. Then, information about the potential relaying devices is broadcasted by the concerned EG device, which uses every member device to adjust routing path(s) in the autonomous system. Furthermore, every EG device has an embedded computational intelligent decision support system that is used to precisely predict the criticality of a neighboring device (preferably relay) in the autonomous systems. Therefore, every device must ensure data transmission via the most reliable path(s), i.e., avoiding critical devices if possible. A device is assumed critical if either its residual energy or received signal strength indicator value is less than the defined threshold values for the autonomous systems. Additionally, the proposed mechanism has ensured a uniform traffic distribution of the transmitted packets in the autonomous system. The operational applicability of the proposed computational intelligence-enabled prediction mechanism in the autonomous system is verified by comparing it with the existing approaches. Simulation results show that the proposed scheme has enhanced the accuracy of the concerned autonomous systems more than other schemes.

A Novel Edge-Based Trust Management System for the Smart City Environment Using Eigenvector Analysis.

The proposed Edge-based Trust Management System (E-TMS) uses an Eigenvector-based approach for eliminating the security threats present in the Internet of Things (IoT) enabled smart city environment. In most existing trust management systems, the trust aggregation process completely depends on the direct trust ratings obtained from both legitimate and malicious neighboring IoT devices. E-TMS possesses an edge-assisted two-level trust computation approach for ensuring the malicious free trust evaluation of IoT devices. The E-TMS aims at removing the false contribution on aggregated trust data. It utilizes the properties of the Eigenvector for identifying compromised IoT devices. The Eigenvector Analysis also helps to avoid false detection. The analysis involves a comparison of all the contributed trust data about every single connected device. A spectral matrix will be generated corresponding to the contributions and the received trust will be scaled based on the obtained spectral values. The absolute sum of obtained values will contain only true contributions. The accurate identification of false data will remove the effect of malicious contributions from the final trust value of a connected IoT device. Since the final trust value calculated by the edge node contains only the trustworthy data, the prediction about the malicious nodes will be accurate. Eventually, the performance of E-TMS has been validated. Throughput and network resilience are higher than the existing system.

Overlapping Coalition Game for Resource Allocation in Many-to-Many D2D Communication

Device-to-device (D2D) communication is one of the promising technologies for the next-generation cellular network, which uses direct communication between two neighboring devices to obtain a high transmission rate. This paper focuses on wireless resource allocation in a many-to-many D2D communication scenario to maximize the D2D transmission sum rate. Through the model analysis, the objective function is formulated while guaranteeing the minimum transmission rate of both the cellular users (CUs) and the D2D pairs. Based on the candidate sequence, an overlapping coalition game algorithm is proposed to enhance the D2D transmission sum rate. Furthermore, the preference sequence of CUs is adopted in the coalition initialization. According to the interference intensity, a power allocation scheme is designed further to improve the transmission sum rate of the D2D pairs. The simulation results have verified the validity of the proposed algorithm.

Multi-Relay Assisted Computation Offloading for Multi-Access Edge Computing Systems With Energy Harvesting

In multi-access edge computing systems with energy harvesting (MEC-EH), the mobile devices are empowered with unstable energy harvested from renewable energy sources. To prolong the life of mobile devices, as many computation-intensive tasks as possible should be offloaded to the MEC server. However, when the system states of mobile device and MEC server are unstable, e.g. poor communication channel conditions, a great number of tasks will be executed locally, leading to a long execution time. Even worse, some tasks may be dropped due to low energy levels. To address this problem, in this paper, we propose a multi-relay assisted computation offloading framework for MEC-EH systems. In this framework, a computation task can be executed by offloading to the MEC server with the help of multiple relay nodes, such as the neighboring nodes. We introduce execution cost as a performance metric to incorporate both the task execution time and task failure. We then develop a low-complexity online algorithm, namely MRACO algorithm, to minimize the average execution cost. MRACO algorithm can select the optimal execution strategy for each task from (1) executing the task locally, (2) offloading it to the MEC server directly, (3) offloading it to the MEC server with the help of the most suitable neighboring nodes, and (4) simply dropping it. Moreover, we also develop an algorithm for selecting the suitable neighboring devices to act as relays and determining the optimal task splitting ratio between them. Finally, performance evaluation shows that the proposed MRACO algorithm greatly outperforms the benchmarks in terms of both average execution time and task drop rate.

Micro grids decentralized hybrid data-driven cuckoo search based adaptive protection model

Micro-grid protection presents great technical challenges to utility company engineers. Several protection schemes have been developed towards a reliable operation on these type of networks, however, the methods are strongly dependent on robust communication systems. This paper presents a decentralized adaptive protection scheme and introduces a data-driven and communication-less approach. The presented solution uses an Artificial Neural Network to train Intelligent Electronic Devices as fault classifiers, also, it uses a cuckoo search metaheuristic to its quasi-optimal adjustment. The Artificial Neural Network enables each classifier to detect faults with only local voltage and current measurements. Presented solution does not assume communication between devices, however, each device brings support to their neighboring devices as back-up protection. Further, system dynamics on the electrical network, such as, changes of topologies, micro-grid status or Distributed Energy Resources outage are considered. The time coordination is simple and easily adjustable due to each fault label of the data-driven model has its own time operation. The presented method is validated on the modified IEEE 34-nodes test feeder. The results of the adaptive protection scheme show accuracy values above of 96% and dependability of 99%. Also, the solution reveals a correlation between the location and the combination of features and hyper-parameters for each Intelligent Electronic Device. The method is development to be easy-to-implement, without hard-to-design parameters, and with highlights potential aspects for real-life applications.

Quality-of-Experience-Aware Incentive Mechanism for Workers in Mobile Device Cloud

Mobile device cloud (MDC) is a collaborative cloud computing platform over which neighboring smart devices form an alliance of shared resources to mitigate resource-scarcity of an individual user device for running compute-intensive applications. A major challenge of such a platform is maximizing user quality-of-experience (QoE) at minimum cost while providing attractive incentives to workers' mobile devices. In state-of-the-art works, either a voluntary task execution or merely resource-cost driven mechanism has been applied to minimize the task execution time while overlooking payment of any additional incentive to the worker devices for their quality services. In this paper, we develop a computational framework for MDC where the afore-mentioned challenging problem is formulated as a multi-objective linear programming (MOLP) optimization function that exploits reverse-auction bidding policy. Due to the NP-hardness of MOLP, we offer two greedy worker selection algorithms for maximizing user QoE or minimizing execution cost. In both algorithms, the amount of incentive awarded to a worker is determined following the QoE offered to a user. Theoretical proofs of desirable properties of the proposed incentive mechanisms are presented. Simulation results illustrate the effectiveness of our incentive algorithms compared to the state-of-the-art approaches.

Signaling Overhead Reduction Techniques in Device-to-Device Communications: Paradigm for 5G and Beyond

Device-to-Device (D2D) communications have recently attracted researchers, attention because of their numerous applications in industry verticals. It enables communications among devices without or with the partial involvement of a central system. To initiate a D2D communications device discovery and radio resource allocation is a critical task when devices have high mobility. Maintaining the quality-of-service and continuous connectivity requires a signaling burden. An efficient mobility management procedure is necessary to discover the neighboring devices in D2D communications systems. The Discovery of a massive number of devices requires an effective radio resource management procedure that causes signaling overhead. In 5G and beyond communication system, two mobility management methods exist; device discovery and beaconing. Since device density and traffic increases exponentially with high mobility, hence device discovery and beaconing increase the signaling overhead and energy consumption in power-limited devices. Thus, signaling overhead research needs much attention in 5G and beyond systems to meet the service requirements like accuracy, latency, and battery life. Therefore, the challenges and the techniques related to signaling overhead in D2D communications are presented.

Developing Action Policies with Q-Learning and Shallow Neural Networks on Reconfigurable Embedded Devices

The size of sensor networks supporting smart cities is ever increasing. Sensor network resiliency becomes vital for critical networks such as emergency response and waste water treatment. One approach is to engineer “self-aware” sensors that can proactively change their component composition in response to changes in work load when critical devices fail. By extension, these devices could anticipate their own termination, such as battery depletion, and offload current tasks onto connected devices. These neighboring devices can then reconfigure themselves to process these tasks, thus avoiding catastrophic network failure. In this article, we compare and contrast two types of self-aware sensors. One set uses Q-learning to develop a policy that guides device reaction to various environmental stimuli, whereas the others use a set of shallow neural networks to select an appropriate reaction. The novelty lies in the use of field programmable gate arrays embedded on the sensors that take into account internal system state, configuration, and learned state-action pairs, which guide device decisions to meet system demands. Experiments show that even relatively simple reward functions develop both Q-learning policies and shallow neural networks that yield positive device behaviors in dynamic environments.

Stress-deformed state of a fixed support of the unit with account of influence of near back technical devices with geometric deviations

The normative and technical documentation in the field of industrial safety establishes permissible deviation standards at which the equipment can be operated at a given technological mode and regular monitoring of the technical condition. As a rule, the main method for detecting deviations is visual and measuring control, as well as geodetic measurements, allowing fixing the admissibility or inadmissibility of geometric deviations. However, the current methods do not provide the calculation of structures with geometric deviations under current operating loads with the determination and assessment of the places of the occurring maximum stresses, as well as their values. The stress-strain state of the process equipment also does not provide the assessment from the effects of neighboring technical devices that are in the common technological system and have various geometric deviations during installation. In this paper, we study the relation of technical devices that are in a common technological system and have geometric deviations such as deviation from verticality and the displacement of the edges by the stress-strain state of the fixed support of a horizontally located apparatus.

ORGANIZATION OF A DECENTRALIZED AND ANONYMOUS MESH NETWORK

As part of this work, proposed the Mesh network architecture, which provides decentralization, security, anonymity and connection of devices without a dedicated router or Internet access. The nodes establish connections directly using Android P2P Wi-Fi technology, which complies with the Wi-Fi Direct standard. P2P Wi-Fi API organizes group communication and allows applications to connect to neighboring devices without the need for an Internet connection or access point. Despite the fact that in the Wi-Fi Direct standard there are no restrictions on connecting groups to each other, the Android Wi-Fi P2P API does not allow organize a connection between several groups. This is due to the fact that the IP addresses of owners of different groups in Wi-Fi Direct are always the same and unchanged. Communication between a P2P client and an inherited client that also owns another group is allowed in both directions. This provides connections between groups, where a P2P client acts as a proxy to access a neighboring group. Thus communication between nodes in a multigroup network is ensured through the use of transport layer tunnels installed in the logical topology and packet switching at the application level. When one or more nodes access the Internet, remote nodes outside the Wi-Fi coverage area communicate with the mesh through the application server. This architecture allows build a corporate, public or home network based on a wireless Mesh network, the nodes of which can be access points, computers and mobile devices of users.

A Reinforcement Learning and Blockchain-Based Trust Mechanism for Edge Networks

Mobile edge computing (MEC) raises the issue of resisting selfish edge attackers that use less computation resources than promised to process offloading tasks or provide faked computation results. In this paper, we present a blockchain based trust mechanism to help MEC address selfish edge attacks and faked service record attacks. This mechanism evaluates the computational performance of the edge devices and broadcasts such information to the neighboring edge devices and mobile devices. By building a reputation assignment method for the edge devices, the edge reputation system chooses the miner of the blockchain, which applies the joint Proof-of-Work and Proof-of-Stake consensus protocol to append a block recording the new service reputations onto the MEC blockchain. We propose a reinforcement learning (RL) based edge central processing unit (CPU) allocation algorithm without knowing the mobile service generation model and the network model in the dynamic edge computing process and a deep RL version to further improve the computational performance. The security performance is analyzed and the performance bound of the edge utility is provided. Experimental results show that this framework suppresses the selfish edge attacks, decreases the response latency and saves the energy compared with a benchmark MEC scheme.

A Reliable Communication and Load Balancing Scheme for Resource-Limited Networks

Sensor-cloud infrastructure provides a storage platform for the massive sensed data, that is flexible and re-configurable, for various application areas which are monitored through the resource-limited networks such as wireless sensor networks (WSNs), ad hoc networks, and Internet of things (IoT). Due to their overwhelming characteristics, these networks are used in different application areas to assist human beings in their daily-life activities. However, these networks have different challenging issues such as reliability in communication and processing, storage of the massive data, efficient utilization of on-board battery, maximum lifetime achievement, minimum possible average packet loss ratio, and reliable routing mechanisms. Although various communication and load balancing mechanisms have been proposed in the literature to resolve this issue, however, these schemes are either application specific or overlay complex. In this paper, a reliable communication and load balancing scheme for the resource-limited networks is presented to resolve these issues, particularly with available resources. To achieve these goals, the proposed scheme bounds every sensing device $C_{i}$ to compute the transmission capabilities of its neighboring devices that is residual energy $E_{r}$ , hop count $H_{c}$ , round trip time ( $RTT_{i}$ ), and processing cost. Initially, to guarantee reliable wireless communication, a source device prefers a neighboring device $C_{i}$ with minimum $H_{c}$ value over those having maximum $H_{c}$ values. Moreover, this scheme bounds every device $C_{i}$ to find four shortest & reliable paths and forward maximum packets on two of these paths preferably on the most reliable and optimal route. Therefore, unlike the traditional shortest path scheme, devices $C_{i}$ reside on these paths do not deplete their on-board battery more rapidly than others. To further improve the reliability of the proposed scheme, the assigned weight-age factors are fine-tuned if one or two of the neighboring devices $C_{i}$ consume 80% of their on-board battery, that is now maximum weight-age is assigned to the residual energy $E_{r}$ and minimum to $H_{c}$ value respectively. Simulation results show the exceptional performance of the proposed reliable communication and load balancing scheme against the field-proven schemes in terms of average packet delivery ratio, average throughput, end-to-end delay, and overall network lifetime.

SYNTHESIS OF STRIP-BAND TELECOMMUNICATION SYSTEM WITH INCREASED CONFIDENTIALITY OF INFORMATION TRANSMISSION THROUGH USE OF PSSEVDOVIDOVYPOVY

Currently, special attention is paid to the creation of broadband telecommunications systems with a noise-like signal. Such systems have indisputable advantages in noise immunity, in the confidentiality of information transmission, electromagnetic compatibility with neighboring electronic devices. The formation of a broadband signal in most cases is carried out by the method of direct spreading of the spectrum by manipulating the carrier frequency signal by a pulse pseudo-random sequence (PVP) –m-sequence, Gold, Kasami, Walsh sequence. Sequences such as a well-known attacker can pick up in the receiver and they can not be considered reliable protection of information. The use of the phenomenon of dynamic chaos provides an opportunity to find a new class of PVP, the structure of which is almost impossible to reproduce, so their use provides increased confidentiality of information transmission. PVP generators are built on the basis of one-dimensional chaotic mappings - logistic, quadratic and cubic, which are chaotic systems. When creating a PVP based on chaos, three secret encryption keys are used: for example, for logistic separation, this is the initial value, the equation parameter, and the start of the sequence. To increase the cryptosecurity of telecommunication systems, it is proposed to create PVP based on 2 chaotic signals (by logistic and quadratic mappings), which provides an increase in secret keys from three to six. The paper proposes a scheme for building a telecommunications system with high confidentiality of information transmission through the use of such PVP.

A Truthful Online Mechanism for Collaborative Computation Offloading in Mobile Edge Computing

Collaborative computation offloading in mobile edge computing where edge users offload tasks opportunistically to resourceful neighboring mobile devices (MDs), offers a promising solution to satisfy low-latency requirements. However, most existing works assume that those MDs volunteer to help edge users without an incentive mechanism. In this article, we propose an auction-based incentive mechanism, where users and MDs participate in the system dynamically. Our auction mechanism runs in the online fashion and optimizes the long-term system welfare without knowledge of future information, e.g., task start time, task length, resource demand, and valuation, etc. We prove that the proposed online mechanism achieves the desired properties, including individual rationality, truthfulness, and computational tractability. Moreover, the theoretical competitive ratio shows that our online mechanism achieves near-optimal long-term social welfare close to the offline optimum. Extensive experiments based on real-world traces demonstrate the efficiency of the proposed online mechanism.

DistLog: A distributed logging scheme for IoT forensics

Digital forensics are vital in the Internet of Things (IoT) domain. This is due to the enormous growth of cyber attacks and their widespread use against IoT devices. While IoT forensics do not prevent IoT attacks, they help in reducing their occurrence by tracing their source, tracking their root causes and designing the corresponding countermeasures. However, modern IoT attacks use anti-forensics techniques to destroy or modify any important digital evidence including log files. Anti-forensics techniques complicate the task for forensic investigators in tracking the attack source. Thus, countermeasures are required to defend against anti-forensics techniques. In this paper, we aim at securing the IoT log files to prevent anti-forensics techniques that target the logs’ availability and integrity such as wiping and injecting attacks. In the proposed solution, and at regular intervals of time, the logs generated by IoT devices are aggregated, compressed and encrypted. Afterwards, the encrypted logs are fragmented, authenticated and distributed over n storage nodes, based on the proposed Modified Information Dispersal Algorithm (MIDA) that can ensure log files availability with a degree of (n−t). For data dispersal, two cases are considered: the case where the fog nodes are interconnected and the case where they are not. For the former case, the n obtained fragments are transmitted to n neighboring IoT devices (aggregation nodes). However, for the latter one, the output is transmitted to the corresponding fog and then, dispersed over the n neighboring fog nodes. A set of security and performance tests were performed showing the effectiveness and robustness of the proposed solution in thwarting well-known security attacks.

Parasitic Cell-to-Cell Heat Transfer in Reram Arrays and Thermally Induced Memory Cell Performance Degradation

Resistive RAM memory are manufactured in a cross-point architecture. A cross-point, where a memory cell is located, is an intersection of vertical and lateral metal lines for the active and inert electrodes of a resistive memory cell. The two metal electrodes are separated by a dielectric such as TaOx. We have manufactured resistive ReRAM cells Cu/TaOx/Pt/Ti and Cu/TaOx/Rh/Cr. The thickness of TaOx is 25 nm. The thickness of the Cu, Pt, Ti, Rh, and Cr layers is 150 nm, 50 nm, 20 nm, 50 nm, 20 nm, respectively. All layers have been deposited using e-beam PVD. The line width of the Cu, Pt/Ti and Rh/Cr varies from 5 μm to 35 μm. The distance between the lines is ca 150 μm. The latter parameter is a characteristic distance for heat transfer between the cells. Fresh isolated Pt/Ti and Rh/Cr devices display similar switching properties, with very similar Vform, Vset, Vreset, and Ron distributions. The inert electrode of Rh/Cr, has roughly twice larger heat conductivity than Pt/Ti. The heat conductivities for Pt, Ti, Rh, and Cr are 72, 22, 150, and 94 W/(mK), respectively, much inferior to the heat conductivity of Cu at 385 W/(mK). When one cell in the array is repeatedly switched on and off, a certain amount of heat is being deposited in the device. The switching heat is dissipated to the environment mainly by the electrode metal lines. Our results show that the heat deposited in a device lingers for some time in the vicinity of the device and spreads to neighboring devices via the common electrode lines. Experiments show that just after repeated set-reset cycles of one device, the performance of neighboring devices is degraded when they have a metal line in common with the heated device. The question became how, in absence of a temperature probe, to measure such heat transfer and the resulting device degradation quantitatively. To probe into such degradation we have chosen specific set conditions of the device at a compliance current (Icc) of 10 uA. For lower Icc, the on-state is volatile, and for larger Icc the cell is non-volatile and the filament is stable, Thus, Icc=10 uA is a stability border case. A fresh device under such condition can be switched in sequence maximally 13-15 times. We find that for devices affected immediately after heating of a neighboring device this maximal number of switching cycles decreases significantly. We have measured the maximum number of switching cycles for neighboring devices along the Cu and along the Pt electrode lines. Because of the higher heat conductivity, the performance degradation for a Cu line extends to more distant neighboring cells than in the case of Pt/Ti electrode lines. After some cooling off period the devices displaying performance degradation recover quickly to the pre-stress performance level. We find that the cooling off period is shorter for the Cu lines than for the Pt/Ti lines. We observe also that the performance impact on the neighboring cells is larger for lines with small cross section (thickness × width) than for those with large cross section. However, for a short time (couple of minutes), the fast heat dissipation extends over a larger distance within which the devices may be affected by the heated device. This poses a dilemma in terms of the sequence, allowable frequency and location of the cells to be programmed. Interestingly, cells which do not share neither of the two metal lines with the heated device (even if geometrically close to it), are mostly unaffected by the heating of the stressed cell, provided there is no direct heat conductor path connecting the device with heat source. This condition is fulfilled when the intermediate cells lying in the path between the monitored device and the heated device are in the off-state. However, if the intermediate cells are in the on-state, they provide a direct electrical (thus a direct heat) path from the heated device to the tested device, leading to the performance degradation of the probed cell. The heat conduction is also affected by the cross-sections of the metal lines. In commercial application the width and thickness of such metal lines are on the order of couple 10's of nm rendering the commercial arrays 100 times tighter in lateral dimensions than our samples. In nanowires the heat transport is no longer diffusive but ballistic which is known to make for a less efficient heat transport. Hence, for commercial memory arrays the nonlocal heating effects and the resulting device performance degradation are likely be more pronounced.

Mobile user behavior based topology formation and optimization in ad hoc mobile cloud

As more and more mobile users use social networks to interact with other people currently, the resource limitation of mobile devices becomes an urgent problem to be solved. However, being a novel type of mobile cloud, the ad hoc mobile cloud allows participating mobile devices to share resources with other neighboring mobile devices, which can help the mobile device process a large amount of compute-intensive applications and conserve energy. In this paper, a mobile user behavior based topology formation and optimization in ad hoc mobile cloud is proposed. The mobile device nodes having similar behavioral features are grouped and formed as an ad hoc mobile cloud, which can reduce network delay and improve efficiency of node interaction. Subsequently, a flower pollination based offloading strategy is presented to reduce response time and save energy consumption. The experiment results show that our proposed ad hoc mobile cloud topology outperforms in scalability, while the offloading algorithm can reduce approximately 25%–50% response time and energy consumption as compared with that of other benchmark algorithms.

Adaptive Frame Skipping With Screen Dynamics for Mobile Screen Sharing Applications

The improvement in hardware capabilities of mobile devices has led to the active use of processor-heavy contents, such as multimedia files, on resource-limited platforms. In addition to simply enjoying such contents on mobile devices, recently commercialized protocols allow the real-time sharing of multimedia (or a mobile device's screen contents) with neighboring devices via wireless connection. However, unlike PC-scale computing platforms, use-case expansions on mobile devices face an additional technical challenge. Specifically, while the improved computation power is capable of handling processor-hungry applications, battery limitations hold back their full utilization. This paper acknowledges the fact that screen sharing on mobile devices can be attractive, but empirically show that minimizing the energy consumption is crutial, and introduces enhancements to the widely used H.264 encoder on mobile devices. Specifically, our enhancements target to minimize the transmission size of multimedia contents by analyzing the screen's dynamics. For contents with high dynamics, our scheme tries to maintain the video quality, while aggressively skipping frames for regions with minimal motion intensity. Our empirical evaluations show that the proposed light-weight enhancements reduce the size of a typical multimedia file by $\sim \hbox{42}$ %, while maintaining a high user-perceived quality (e.g., Structural SIMilarity measure) of 0.925. Wirelessly sharing this video results in $\sim \hbox{31}$ % lower frame transmission rate and up to $\sim \hbox{21}$ % power savings in less dynamic regions.