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ICESS 2026: International Conference on Embedded Software and Systems

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投稿締切日:
2026-07-15
通知日:
2026-09-10
開催日:
2026-10-29
開催地:
Luoyang, China
開催回数:
閲覧: 36830   フォロー: 4   参加: 2

会伴インデックス (CP-I)

55.6 / 100
全 5,687 件中 第 810 位 · 上位 15%

ソフトウェア工学 分野 247 件中 第 85 位 システム・アーキテクチャ 分野 341 件中 第 116 位

学術的評価 (35%) データなし — 中立の基準値 50 点として算入 —
投稿の選択性 (20%) データなし — 中立の基準値 50 点として算入 —
開催回数 (20%)
84
コミュニティの注目度 (10%)
30
公開情報の充実度 (15%)
55

使用した入力: 確認できる開催回数：22 · 会伴でフォローしている研究者：4 人 · 過去 24 か月にこのページを開いた研究者：6 人

公開情報で不足しているもの: 過去の採択率 (+4.5) · 最優秀論文の記録 (+2.3)
主催者は会議を認証申請したうえで、このページから直接追加できます。スコアは毎晩再計算されます。このスコアを上げるには

信頼度 45% — スコアのうち、中立の基準値ではなく実際に観測されたデータに基づく割合。 このスコアの算出方法 · ランキングを見る · アルゴリズム版 1.1 · 算出日 2026-09-27

論文募集

ICESS 2026 (International Conference on Embedded Software and Systems) is an academic conference held in Luoyang, China on 2026-10-29. The paper submission deadline is 2026-07-15. Acceptance notifications are sent on 2026-09-10.

IEEE ICESS-2026, the International Conference on Embedded Software and Systems, serves as a global forum for researchers and developers from academia, industry, and government to present and discuss emerging ideas and trends in embedded software and systems. The conference has a broad scope covering the design, implementation, optimization, and validation of embedded software and systems across diverse application domains. With the rapid development of high-performance embedded chips, the computing capabilities of edge devices have been significantly enhanced, enabling them to handle computation-intensive tasks in real time. As a result, an increasing number of artificial intelligence (AI) applications are being deployed on edge computing platforms to ensure low-latency responses and efficient processing. The integration of edge computing and AI has led to the emergence of edge intelligence, an innovative paradigm that combines networking, computing, storage, and application capabilities. By deploying intelligent algorithms closer to end users, edge intelligence enables more efficient and responsive intelligent services. Compared with cloud-based AI models, edge intelligence offers advantages such as lower power consumption, reduced latency, enhanced security, and improved user proximity. Consequently, edge intelligence has become a prominent research topic in recent years. In parallel, leading global enterprises such as Huawei, Google, Microsoft, Amazon, and Cisco are actively investing in this area to accelerate advancements in precision agriculture, smart healthcare, smart cities, industrial Internet of Things, and related industries. IEEE ICESS-2026 will be held in Luoyang, China, a historic city known for its rich cultural heritage and as one of the ancient capitals of China. The city is home to world-renowned landmarks such as the Longmen Grottoes and the White Horse Temple, offering an inspiring environment that blends tradition with modern innovation. The focus of ICESS-2026 is to present novel research contributions that advance the state of the art and deepen the understanding of challenges in embedded software and systems, particularly in areas related to edge intelligence, including AI model deployment, task scheduling, resource management, system design and optimization, collaborative methodologies, and integrated software and system solutions. Prospective authors are invited to submit their papers to ICESS-2026. Accepted papers will be submitted for inclusion into IEEE Xplore, subject to meeting IEEE Xplore’s scope and quality requirements. Authors of selected best papers will be invited to extend their contributions for special issues of prestigious journals planned in conjunction with the conference. Topics of interest include, but are not limited to: Track 1: Edge Intelligence Embedded systems and software for edge intelligence Real-time systems and software for edge intelligence Multimodal sensing fusion systems and software for edge intelligence AI chip design for edge intelligence Lightweight AI model design for edge intelligence Task scheduling and resource management for edge intelligence Low-power and high-reliability designs for edge intelligence Embedded AI model training methods AI framework design and optimization for edge devices Intelligent sensing, interaction, and decision system design Edge–edge, edge–cloud, and end–edge–cloud collaborative methodologies Model, operator, and hardware optimization for robotics, UAVs, and autonomous driving Other edge intelligence systems and applications Track 2: Systems, Models and Algorithms Embedded system architectures Embedded software architectures Embedded operating systems, scheduling, and runtime support Embedded storage and I/O systems Real-time embedded systems Distributed and networked embedded systems Fault-tolerant and trusted embedded systems Power- and thermal-aware computing Mixed-criticality embedded systems Heterogeneous SoC and multicore embedded systems Reconfigurable embedded computing Track 3: Design Methodology and Tools Design technologies of embedded systems Formal methods for embedded systems Middleware for embedded systems Integrated development environments and software tools Hardware/software co-design Component-based embedded software design Model-based design for embedded software Domain- and application-specific design techniques Testing techniques for embedded software and systems Verification and validation for embedded systems Compilation and debugging techniques and tools Performance evaluation techniques and tools Safety of machine learning for embedded systems Track 4: Emerging Embedded Applications and Interdisciplinary Topics Intelligent embedded systems Machine learning for embedded applications Internet of Things (IoT) Wearable computing Smart city applications Intelligent traffic signal control systems Robotics and control systems Wireless sensor networks Cyber-physical systems (CPS) Assured autonomy for safety-critical CPS Automotive and avionics systems Medical systems Database and multimedia systems Network protocols and security Emergency and disaster management Consumer electronics, mobile cloud computing, and approximate computing Industrial practices and case studies
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