Towards Resilience: Slice-Driven Orchestration of Mobile IoMT in 6G-Edge-Continuum

Muhammad Zeeshan; Hafiz Faheem Shahid; Erkki Harjula

cs.AIOct 12, 2026

Towards Resilience: Slice-Driven Orchestration of Mobile IoMT in 6G-Edge-Continuum

Authors: Muhammad Zeeshan, Hafiz Faheem Shahid, Erkki Harjula

Organizations: Centre for Wireless Communication, University of Oulu, Oulu, Finland

Abstract

Emerging healthcare services, such as real-time diagnostics, remote health monitoring and computer-assisted surgery, require stringent Quality of Service (QoS) guarantees while operating under limited and heterogeneous edge resources. Existing Internet of Medical Things (IoMT) orchestration methods primarily focus on communication metrics such as latency and bandwidth, while overlooking edge resource constraints and dynamic adaptation to workload and user mobility. This paper presents a novel resource-slicing-based adaptive orchestration framework that integrates 5G network slicing (NS), resource-aware orchestration, and mobility-aware task offloading into a single adaptive system. IoMT services are assigned to distinct 5G network slices while jointly considering CPU capacity, memory availability and amount of storage, enabling more efficient service placement and execution. In addition, the system supports user mobility and dynamic task offloading to maintain service continuity under changing network conditions. SimPy-based simulation demonstrates that the proposed method enhances service deployment efficiency, reduces request blocking, and improves user acceptance while maintaining reliable performance across diverse IoMT scenarios.

Figures & tables

Fig. 1: System architecture for IoMT application orchestration.

Service	CPU	GPU	RAM	Storage	BW	Latency	Slice
	(Cores)	(Cores)	(MB)	(MB)	(Mbps)	(ms)
Doctor on Demand	6	1–4	4096	2048	10–150	$\leq$ 150	eMBB
XR-assisted Surgery	8	1–8	8192	4096	10–100	$\leq$ 20	uRLLC
Wearable Monitoring	2	–	1024	512	1–2	$\leq$ 100	mMTC

TABLE I: Resource requirements and network slice mapping for IoMT services.

Fig. 2: IoMT users association with nearest base stations across regions in the proposed system.

Fig. 3: Cumulative served applications per network slice across base stations.

From/To	BS 0		BS 1		BS 2		BS 3		BS 4		BS 5		BS 6
From/To	P	B	P	B	P	B	P	B	P	B	P	B	P	B
BS 0	0	0	31	32	29	28	32	30	38	42	0	0	0	0
BS 1	18	23	0	0	0	0	0	0	19	12	0	0	6	6
BS 2	19	15	0	0	0	0	22	16	1	0	10	7	0	0
BS 3	25	21	0	0	13	13	0	0	0	0	0	0	0	0
BS 4	25	27	13	9	1	1	0	0	0	0	0	0	0	0

TABLE II: Base station handover events during user movement.

Fig. 4: Comparison of average resource utilization between the proposed and baseline systems for (a) CPU, (b) GPU, (c) Storage, and (d) RAM.

Fig. 5: Performance metrics: (a) Connectivity ratio, (b) Bandwidth utilization, (c) Acceptance ratio, and (d) Handover count.

Network Metrics	Proposed	Baseline
Total Users	500	500
Average Connectivity Ratio	0.85	0.59
Average BW Usage (Mbps)	6241	10504
Average Service Acceptance Ratio	0.97	0.47
Average Handover Count	3.08	2.88

TABLE III: COMPARISON OF NETWORK PERFORMANCE METRICS.

Network Slicing

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May 21, 2026J/K move · Enter open · S save

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6G Networks Edge Computing Virtual Reality Network Slicing Wireless Resource Allocation

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Virtual Reality (VR) services delivered over 6G networks demand ultra-low latency and high bandwidth to ensure seamless user experiences. This paper presents an intelligent resource allocation and edge caching framework for 6G O-RAN networks, leveraging Deep Q-Network (DQN) learning for optimizing edge caching and dynamic resource provisioning across multiple network slices within an O-RAN-compliant architecture. By incorporating DRL agents into the network control plane, the proposed system enables proactive and adaptive content distribution as well as real-time computational resource allocation that meets the quality-of-service demands of eMBB, URLLC, and especially the emerging MBRLLC slices essential for VR. Simulation results demonstrate that the DQN-based framework consistently outperforms traditional methods in reducing latency and improving throughput, leading to more reliable and responsive support for immersive VR applications in 6G environments.

Abstract

Network Slicing

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