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Due to time required for institutional approvals, this year’s SenSys will retain the current name (Conference on Embedded Networked Sensor Systems) and will be collocated with CPS-IoT Week 2025. All accepted papers this year will be published in the ACM SenSys proceedings. SenSys 2025 invites groundbreaking research contributions on networked sensor systems, embedded systems, mobile computing, the Internet of Things (IoT), wireless communications, signal processing, machine learning, cyber-physical systems, and AI-driven applications. This newly merged premier conference unifies the strengths of SenSys, IoTDI, and IPSN, bringing together researchers and practitioners from academia, industry, and government to discuss innovative advances in sensing, artificial intelligence, and systems that enable future embedded computing environments. Topics of Interest: We invite submissions on a broad range of topics that have been covered by SenSys, IPSN, and IoTDI, and new emerging topics of interest. The topics of interest include, but are not limited to: 1. Sensors and Networked Sensing Systems Novel sensor technologies and deployments Heterogeneous sensor networks and data fusion Edge computing, fog computing, and real-time IoT/CPS systems Localization, synchronization, RFID, and RF sensing VLC and visible light-based sensing Digital twins for real-world systems and applications 2. Platforms, Networking, and Communication Protocols New communication paradigms for ubiquitous connectivity Systems for extreme environments (e.g., underwater, aerial, space) Low-power wireless protocols Novel IoT protocols and network abstractions, including for 5G/6G and other architectures Satellite systems and applications, including CubeSats 3. Embedded AI, Information Processing, and Machine Learning Resource-efficient machine learning for embedded and mobile platforms Information processing in sensor networks and embedded systems Large foundational models and their distillation to edge platforms Large language models and multi-modal large models for embedded systems Federated learning, neural architecture search for edge devices Collaborative sensing with AI-driven inference models 4. Algorithms, Data, and Theory Analytic foundations for embedded sensing systems Data related issues, such as methods, tools, and analysis Coding, compression, and information theory Theoretical foundations and fundamental bounds 5. Applications and Real-world Deployments Smart cities, smart buildings, and industrial IoT (IIoT) AR/VR and metaverse-inspired challenges Autonomous vehicles, unmanned aerial vehicles (UAVs), and drones Applications in healthcare, fitness, and wellness Personal, wearable, and other human-centric embedded systems Experiences from real-world deployments and lessons learned 6. Security, and Privacy in Embedded Sensing Systems Secure and privacy-sensitive sensing systems IoT data marketplaces, compression, and semantic summarization Decentralization and blockchain for embedded sensing systems Secure communications for the IoT Fault-tolerance, dependability, and robustness in embedded platforms and applications Fairness, equity, and transparency issues in IoT and CPS 7. Novel Paradigms and Architectures for Embedded Sensing Systems Energy-efficient, sustainable system designs Edge AI and its implications for system latency, power, and complexity Human-machine interaction, including gesture recognition and voice-based systems IoT and CPS for sustainability, such as smart grids and energy management</pre><div class="alert alert-warning"> Last updated by <a href="/researcher/1">Dou Sun</a> in 2024-10-01</div> </div> </div>  <div class="portlet" id="yw2"> <div class="portlet-decoration"> <div class="portlet-title"><i class="icon icon-signal"></i> Acceptance Ratio</div> </div> <div class="portlet-content"> <div class="row-fluid"> <div class="span8"> <div id="placeholder" style="height:240px"></div> <script type="text/javascript"> $(function() { var data = [ ['2003', 17.5], ['2004', 14.5], ['2005', 17.8], ['2006', 19.7], ['2007', 16.8], ['2008', 16.3], ['2009', 17.6], ['2010', 17.2], ['2011', 19.5], ['2017', 17.2] ]; var ticks = [ ['2003', '2003'], ['2004', '2004'], ['2005', '2005'], ['2006', '2006'], ['2007', '2007'], ['2008', '2008'], ['2009', '2009'], ['2010', '2010'], ['2011', '2011'], ['2017', '2017'] ]; $.plot("#placeholder", [ data ], { series: { bars: { show: true, barWidth: 0.6, align: "center" } }, xaxis: { ticks: ticks, tickLength: 0 }, yaxis: { max: 100, tickFormatter: function (v, axis) { return v + "%"; } } }); }); </script> </div> <div class="span4"> <div style="height:240px;overflow:scroll"> <div id="yw3" class="grid-view"> <table class="table table-hover table-striped table-bordered table-condensed"> <thead> <tr> <th id="yw3_c0">Year</th><th id="yw3_c1">Submitted</th><th id="yw3_c2">Accepted</th><th id="yw3_c3">Accepted(%)</th></tr> </thead> <tbody> <tr class="odd"> <td>2017</td><td>151</td><td>26</td><td>17.2%</td></tr> <tr class="even"> <td>2011</td><td>123</td><td>24</td><td>19.5%</td></tr> <tr class="odd"> <td>2010</td><td>145</td><td>25</td><td>17.2%</td></tr> <tr class="even"> <td>2009</td><td>119</td><td>21</td><td>17.6%</td></tr> <tr class="odd"> <td>2008</td><td>153</td><td>25</td><td>16.3%</td></tr> <tr class="even"> <td>2007</td><td>149</td><td>25</td><td>16.8%</td></tr> <tr class="odd"> <td>2006</td><td>122</td><td>24</td><td>19.7%</td></tr> <tr class="even"> <td>2005</td><td>118</td><td>21</td><td>17.8%</td></tr> <tr class="odd"> <td>2004</td><td>145</td><td>21</td><td>14.5%</td></tr> <tr class="even"> <td>2003</td><td>137</td><td>24</td><td>17.5%</td></tr> </tbody> </table><div class="keys" style="display:none" title="/conference/61"><span>3452</span><span>718</span><span>717</span><span>716</span><span>715</span><span>714</span><span>713</span><span>712</span><span>711</span><span>710</span></div> </div></div> </div> </div> </div> </div> <div class="portlet" id="yw4"> <div class="portlet-decoration"> <div class="portlet-title"><i class="icon icon-thumbs-up"></i> Best Papers</div> </div> <div class="portlet-content"> <div id="yw5" class="grid-view"> <table class="table table-hover table-striped table-bordered table-condensed"> <thead> <tr> <th id="yw5_c0">Year</th><th id="yw5_c1">Best Papers</th></tr> </thead> <tbody> <tr class="odd"> <td>2020</td><td><a target="_blank" href="http://scholar.google.com/scholar?q=Enabling+Distributed+Solar+Energy+Trading+in+Residential+Virtual+Power+Plants">Enabling Distributed Solar Energy Trading in Residential Virtual Power Plants</a></td></tr> <tr class="even"> <td>2020</td><td><a target="_blank" href="http://scholar.google.com/scholar?q=Deep+compressive+offloading%3A+speeding+up+neural+network+inference+by+trading+edge+computation+for+network+latency">Deep compressive offloading: speeding up neural network inference by trading edge computation for network latency</a></td></tr> <tr class="odd"> <td>2020</td><td><a target="_blank" href="http://scholar.google.com/scholar?q=Zero-wire%3A+a+deterministic+and+low-latency+wireless+bus+through+symbol-synchronous+transmission+of+optical+signals">Zero-wire: a deterministic and low-latency wireless bus through symbol-synchronous transmission of optical signals</a></td></tr> <tr class="even"> <td>2018</td><td><a target="_blank" href="http://scholar.google.com/scholar?q=3D+Localization+for+Sub-Centimeter+Sized+Devices">3D Localization for Sub-Centimeter Sized Devices</a></td></tr> <tr class="odd"> <td>2011</td><td><a target="_blank" href="http://scholar.google.com/scholar?q=Aveksha%3A+A+Hardware-Software+Approach+for+Non-intrusive+Tracing+and+Profiling+of+Wireless+Embedded+Systems">Aveksha: A Hardware-Software Approach for Non-intrusive Tracing and Profiling of Wireless Embedded Systems</a></td></tr> </tbody> </table><div class="keys" style="display:none" title="/conference/61"><span>3979</span><span>3980</span><span>3981</span><span>3414</span><span>1263</span></div> 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